加藤言人 2020年5月21日
## Read Data
d <- readRDS("../data_public/main_data_v5.rds")
#d <- read_dta("../data_public/main_data_v6.dta", encoding="UTF-8")
nrow(d)## [1] 1240
##
## 0 1
## 1201 39
## [1] 568.70000 84.55000 75.63333 75.28333 74.76667 73.08333 64.71667 58.91667
## [9] 55.40000 50.83333 48.35000 47.83333 47.58333 47.26667 47.23333 45.21667
## [17] 43.53333 42.53333 40.80000 40.78333
d <- d[which(d$satisficer==0),] # Not Satisficers
d <- d[which(d$surveytime<=90),] # Took too long (only one)
nrow(d)## [1] 1200
## Drop Respondents with Missing Value in Demographic Questions
dtmp <- d[complete.cases(d[,c("knall","fem","age","lvlen","ownh",
"edu3","wk","mar","cld")]),]
nrow(dtmp)## [1] 1123
# Exporting Tables
exporttab <- function(tmp,filename){
tab <- paste0(sprintf("%.1f",(tmp/sum(tmp))*100),"%")
names(tab) <- names(tmp)
tab <- kable(t(tab), format="latex", align="c")
writeLines(tab, con=paste0("../out/",filename))
return(tmp)
}
# Knowledge
tmp <- table(dtmp$knall)
names(tmp) <- round(as.numeric(names(tmp)),3)
exporttab(tmp, "demtab_knall.tex")## 0 0.143 0.286 0.429 0.571 0.714 0.857 1
## 94 156 220 187 158 133 110 65
##
## 0 0.5 1
## 545 4 574
# Age (Table by Cohort)
tmp <- table(floor(dtmp$age/10))
names(tmp) <- c("18-19","20-29","30-39","40-49","50-59","60-69","70-79")
exporttab(tmp, "demtab_age.tex")## 18-19 20-29 30-39 40-49 50-59 60-69 70-79
## 25 301 392 270 106 23 6
##
## 0 1 2 3 4
## 267 236 125 317 178
##
## 0 1
## 500 623
# Education
tmp <- table(dtmp$edu3)
names(tmp) <- c("小/中/高","高専/専門学校","大学/大学院")
exporttab(tmp, "demtab_edu3.tex")## 小/中/高 高専/専門学校 大学/大学院
## 211 205 707
tmp <- readLines("../out/demtab_edu3.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/demtab_edu3.tex", useBytes = TRUE)
# 就業の有無
exporttab(table(dtmp$wk), "demtab_wk.tex")##
## 0 1
## 294 829
##
## 0 1
## 642 481
##
## 0 1
## 788 335
ctab <- cor(dtmp[,c("ide_self","ide_psup","ide_iss_1","ide_iss_2")])
ctab[upper.tri(ctab)] <- NA
colnames(ctab) <- rownames(ctab) <- c("自己申告","政党支持","外交安全保障","権利機会平等")
round(ctab,3)## 自己申告 政党支持 外交安全保障 権利機会平等
## 自己申告 1.000 NA NA NA
## 政党支持 0.356 1.000 NA NA
## 外交安全保障 0.362 0.439 1.000 NA
## 権利機会平等 0.266 0.142 0.025 1
print(xtable(ctab, digits=3,caption="イデオロギー指標間の相関(「わからない」回答は中間値)"),
caption.placement="top", file="../out/coride.tex")
tmp <- readLines("../out/coride.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/coride.tex", useBytes = TRUE)ctab <- cor(dtmp[,c("ide_self_mis","ide_psup_mis","ide_iss_mis_1","ide_iss_mis_2")],
use="pairwise")
ctab[upper.tri(ctab)] <- NA
colnames(ctab) <- rownames(ctab) <- c("自己申告","政党支持","外交安全保障","権利機会平等")
round(ctab,3)## 自己申告 政党支持 外交安全保障 権利機会平等
## 自己申告 1.000 NA NA NA
## 政党支持 0.379 1.000 NA NA
## 外交安全保障 0.393 0.455 1.000 NA
## 権利機会平等 0.337 0.215 0.062 1
print(xtable(ctab, digits=3,caption="イデオロギー指標間の相関(「わからない」回答を除外)"),
caption.placement="top", file="../out/coride_mis.tex")
tmp <- readLines("../out/coride_mis.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/coride_mis.tex", useBytes = TRUE)tab <- table(dtmp$easing_opi)/sum(table(dtmp$easing_opi))
tab <- data.frame(prop = as.numeric(tab),
names = c("反対\n(-3)","-2","-1",
"どちらともいえない\n/無回答(0)",
"1","2","賛成\n(3)"))
tab$names <- factor(tab$names, levels=tab$names)
p <- ggplot(tab, aes(x=names,y=prop)) +
geom_bar(stat="identity") +
ylab("回答割合") + xlab(NULL) +
#ggtitle("金融緩和に対する意見の回答分布") +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.text.x = element_text(size=12, face="bold"))
ptab <- table(dtmp$easing_opi_mis)/sum(table(dtmp$easing_opi_mis))
tab <- data.frame(prop = as.numeric(tab),
names = c("反対\n(-3)","-2","-1",
"どちらともいえない\n/無回答(0)",
"1","2","賛成\n(3)"))
tab$names <- factor(tab$names, levels=tab$names)
p <- ggplot(tab, aes(x=names,y=prop)) +
geom_bar(stat="identity") +
ylab("回答割合") + xlab(NULL) +
#ggtitle("金融緩和に対する意見の回答分布") +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.text.x = element_text(size=12, face="bold"))
ptab <- table(dtmp$ide_self)/sum(table(dtmp$ide_self))
tab <- data.frame(prop = as.numeric(tab),
names = c("左派/\nリベラル\n(-3)","-2","-1",
"中立\n(0)","1","2","右派/\n保守\n(3)"))
tab$names <- factor(tab$names, levels=tab$names)
p1 <- ggplot(tab, aes(x=names,y=prop)) +
geom_bar(stat="identity") +
scale_y_continuous(limits=c(0,0.5)) +
ylab(NULL) + xlab(NULL) +
ggtitle("自己申告\nイデオロギー\n(度数分布)") +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.text.x = element_text(size=12, face="bold"))
p1tab <- table(dtmp$ide_self_mis)/sum(table(dtmp$ide_self_mis))
tab <- data.frame(prop = as.numeric(tab),
names = c("左派/\nリベラル\n(-3)","-2","-1",
"中立\n(0)","1","2","右派/\n保守\n(3)"))
tab$names <- factor(tab$names, levels=tab$names)
p1_mis <- ggplot(tab, aes(x=names,y=prop)) +
geom_bar(stat="identity") +
scale_y_continuous(limits=c(0,0.5)) +
ylab(NULL) + xlab(NULL) +
ggtitle("自己申告\nイデオロギー\n(度数分布)") +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.text.x = element_text(size=12, face="bold"))
p1_misp2_1 <- ggplot(dtmp, aes(x=ide_iss_1,y=..count../sum(..count..))) +
geom_histogram(bins=10,color="white") +
scale_y_continuous(limits=c(0,0.5)) +
ylab(NULL) + xlab(NULL) +
ggtitle("外交安全保障\nイデオロギー\n(ヒストグラム)") +
scale_x_continuous(breaks=c(-3,-2,-1,0,1,2,3),
limits=c(-3,3),
labels=c("左派\n(-3)\n","-2","-1","0","1","2","右派\n(3)\n")) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.text.x = element_text(size=12, face="bold"))
p2_1## Warning: Removed 2 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
p2_2 <- ggplot(dtmp, aes(x=ide_iss_2,y=..count../sum(..count..))) +
geom_histogram(bins=10,color="white") +
scale_y_continuous(limits=c(0,0.5)) +
ylab(NULL) + xlab(NULL) +
ggtitle("権利機会平等\nイデオロギー\n(ヒストグラム)") +
scale_x_continuous(breaks=c(-3,-2,-1,0,1,2,3),
limits=c(-3,3),
labels=c("左派\n(-3)\n","-2","-1","0","1","2","右派\n(3)\n")) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.text.x = element_text(size=12, face="bold"))
p2_2## Warning: Removed 15 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
p2_1_mis <- ggplot(dtmp, aes(x=ide_iss_mis_1,y=..count../sum(..count..))) +
geom_histogram(bins=10,color="white") +
scale_y_continuous(limits=c(0,0.5)) +
ylab(NULL) + xlab(NULL) +
ggtitle("外交安全保障\nイデオロギー\n(ヒストグラム)") +
scale_x_continuous(breaks=c(-3,-2,-1,0,1,2,3),
limits=c(-3,3),
labels=c("左派\n(-3)\n","-2","-1","0","1","2","右派\n(3)\n")) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.text.x = element_text(size=12, face="bold"))
p2_1_mis## Warning: Removed 448 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
p2_2_mis <- ggplot(dtmp, aes(x=ide_iss_mis_2,y=..count../sum(..count..))) +
geom_histogram(bins=10,color="white") +
scale_y_continuous(limits=c(0,0.5)) +
ylab(NULL) + xlab(NULL) +
ggtitle("権利機会平等\nイデオロギー\n(ヒストグラム)") +
scale_x_continuous(breaks=c(-3,-2,-1,0,1,2,3),
limits=c(-3,3),
labels=c("左派\n(-3)\n","-2","-1","0","1","2","右派\n(3)\n")) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.text.x = element_text(size=12, face="bold"))
p2_2_mis## Warning: Removed 457 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
tab <- table(dtmp$ide_psup)/sum(table(dtmp$ide_psup))
tab <- data.frame(prop = as.numeric(tab),
names = c("左派\n政党支持\n(-1)","無党派\nその他\n(0)",
"右派\n政党支持\n(1)"))
tab$names <- factor(tab$names, levels=tab$names)
p3 <- ggplot(tab, aes(x=names,y=prop)) +
geom_bar(stat="identity") +
scale_y_continuous(limits=c(0,0.5)) +
ylab(NULL) + xlab(NULL) +
ggtitle("政党支持\nイデオロギー\n(度数分布)") +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.text.x = element_text(size=12, face="bold"))
p3tab <- table(dtmp$ide_psup_mis)/sum(table(dtmp$ide_psup_mis))
tab <- data.frame(prop = as.numeric(tab),
names = c("左派\n政党支持\n(-1)","無党派\nその他\n(0)",
"右派\n政党支持\n(1)"))
tab$names <- factor(tab$names, levels=tab$names)
p3_mis <- ggplot(tab, aes(x=names,y=prop)) +
geom_bar(stat="identity") +
scale_y_continuous(limits=c(0,0.5)) +
ylab(NULL) + xlab(NULL) +
ggtitle("政党支持\nイデオロギー\n(度数分布)") +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.text.x = element_text(size=12, face="bold"))
p3_mis## Warning: Removed 2 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
## Warning: Removed 15 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
## Warning: Removed 448 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
## Warning: Removed 457 rows containing non-finite values (stat_bin).
## Warning: Removed 1 rows containing missing values (geom_bar).
p12cor_1 <- ggplot(dtmp, aes(x=ide_self,y=ide_iss_1)) +
geom_jitter(alpha=0.6, width=0.2, height=0.2,size=2) +
ylab("外交安全保障イデオロギー") +
xlab("自己申告イデオロギー") +
annotate("text", x=-2.5, y=3.2, size=4.5,
label=paste("r =",round(cor(dtmp$ide_self,dtmp$ide_iss_1),3))) +
scale_x_continuous(breaks=c(-3,-2,-1,0,1,2,3),
labels=c("左派/\nリベラル\n(-3)","-2","-1",
"中立\n(0)","1","2","右派/\n保守\n(3)")) +
scale_y_continuous(breaks=c(-3,-2,-1,0,1,2,3),
#limits=c(-3,3),
labels=c("左派(-3)","-2","-1","0","1","2","右派(3)")) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.title = element_text(size=12, face="bold"),
axis.text = element_text(size=12, face="bold"))
p12cor_1p12cor_2 <- ggplot(dtmp, aes(x=ide_self,y=ide_iss_2)) +
geom_jitter(alpha=0.6, width=0.2, height=0.2,size=2) +
ylab("権利機会平等イデオロギー") +
xlab("自己申告イデオロギー") +
annotate("text", x=-2.5, y=3.5, size=4.5,
label=paste("r =",round(cor(dtmp$ide_self,dtmp$ide_iss_2),3))) +
scale_x_continuous(breaks=c(-3,-2,-1,0,1,2,3),
labels=c("左派/\nリベラル\n(-3)","-2","-1",
"中立\n(0)","1","2","右派/\n保守\n(3)")) +
scale_y_continuous(breaks=c(-3,-2,-1,0,1,2,3),
#limits=c(-3,3),
labels=c("左派(-3)","-2","-1","0","1","2","右派(3)")) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.title = element_text(size=12, face="bold"),
axis.text = element_text(size=12, face="bold"))
p12cor_2ggplot() + theme_void()
p12cor <- arrangeGrob(p12cor_1 + xlab(NULL) + ylab(NULL) + ggtitle("外交安全保障"),
p12cor_2 + xlab(NULL) + ylab(NULL) + ggtitle("権利機会平等"),
nrow=1, left="争点態度イデオロギー",
bottom="自己申告イデオロギー")
grid.draw(p12cor)p12cor_1 <- ggplot(dtmp, aes(x=ide_self_mis,y=ide_iss_mis_1)) +
geom_jitter(alpha=0.6, width=0.2, height=0.2,size=2) +
ylab("外交安全保障イデオロギー") +
xlab("自己申告イデオロギー") +
annotate("text", x=-2.5, y=3.2, size=4.5,
label=paste("r =",round(cor(dtmp$ide_self_mis,dtmp$ide_iss_mis_1, use="pairwise"),3))) +
scale_x_continuous(breaks=c(-3,-2,-1,0,1,2,3),
labels=c("左派/\nリベラル\n(-3)","-2","-1",
"中立\n(0)","1","2","右派/\n保守\n(3)")) +
scale_y_continuous(breaks=c(-3,-2,-1,0,1,2,3),
#limits=c(-3,3),
labels=c("左派(-3)","-2","-1","0","1","2","右派(3)")) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.title = element_text(size=12, face="bold"),
axis.text = element_text(size=12, face="bold"))
p12cor_1## Warning: Removed 496 rows containing missing values (geom_point).
p12cor_2 <- ggplot(dtmp, aes(x=ide_self_mis,y=ide_iss_mis_2)) +
geom_jitter(alpha=0.6, width=0.2, height=0.2,size=2) +
ylab("権利機会平等イデオロギー") +
xlab("自己申告イデオロギー") +
annotate("text", x=-2.5, y=3.5, size=4.5,
label=paste("r =",round(cor(dtmp$ide_self_mis,dtmp$ide_iss_mis_2, use="pairwise"),3))) +
scale_x_continuous(breaks=c(-3,-2,-1,0,1,2,3),
labels=c("左派/\nリベラル\n(-3)","-2","-1",
"中立\n(0)","1","2","右派/\n保守\n(3)")) +
scale_y_continuous(breaks=c(-3,-2,-1,0,1,2,3),
#limits=c(-3,3),
labels=c("左派(-3)","-2","-1","0","1","2","右派(3)")) +
theme_bw() +
theme(plot.title = element_text(hjust=0.5, face="bold"),
axis.title = element_text(size=12, face="bold"),
axis.text = element_text(size=12, face="bold"))
p12cor_2## Warning: Removed 496 rows containing missing values (geom_point).
ggplot() + theme_void()
p12cor <- arrangeGrob(p12cor_1 + xlab(NULL) + ylab(NULL) + ggtitle("外交安全保障"),
p12cor_2 + xlab(NULL) + ylab(NULL) + ggtitle("権利機会平等"),
nrow=1, left="争点態度イデオロギー",
bottom="自己申告イデオロギー")## Warning: Removed 496 rows containing missing values (geom_point).
## Warning: Removed 496 rows containing missing values (geom_point).
dmod <- data.frame(
ide_self = dtmp$ide_self,
ide_self_mis = dtmp$ide_self_mis,
ide_psup = dtmp$ide_psup,
ide_psup_mis = dtmp$ide_psup_mis,
ide_iss_1 = dtmp$ide_iss_1,
ide_iss_mis_1 = dtmp$ide_iss_mis_1,
ide_iss_2 = dtmp$ide_iss_2,
ide_iss_mis_2 = dtmp$ide_iss_mis_2,
knall = dtmp$knall,
fem = dtmp$fem,
age = dtmp$age,
lvlen = dtmp$lvlen,
ownh = dtmp$ownh,
edu3_1 = ifelse(dtmp$edu3==0, 1, 0),
edu3_2 = ifelse(dtmp$edu3==1, 1, 0),
edu3_3 = ifelse(dtmp$edu3==2, 1, 0),
wk = dtmp$wk,
mar = dtmp$mar,
cld = dtmp$cld
)
for(i in 1:ncol(dmod)) {
dmod[,i] <- (dmod[,i] - mean(dmod[,i], na.rm=TRUE))/sd(dmod[,i], na.rm=TRUE)
}
hypnames <- c("H1:1.経済成長\nv.s.統制群",
"H2:2.経済成長&貧困削減\nv.s.1.経済成長",
"H2X:2X.経済成長&格差縮小\nv.s.1.経済成長",
"H3:3.経済成長&学者賛成\nv.s.1.経済成長",
"H3:4.成長&貧困&学者\nv.s.2.経済成長&貧困削減")
# H1
res1_h1 <- apply(dmod, 2, function(x) coeftest(lm(x~dtmp$g_h1),vcov.=vcovHC(lm(x~dtmp$g_h1),type="HC2")))
res2_h1 <- as.data.frame(t(apply(res1_h1, 2, function(x) c(x[2],x[4],x[8]))))
colnames(res2_h1) <- c("coef","se","pval")
res2_h1$hyp <- hypnames[1]
# H2
res1_h2 <- apply(dmod, 2, function(x) coeftest(lm(x~dtmp$g_h2),vcov.=vcovHC(lm(x~dtmp$g_h2),type="HC2")))
res2_h2 <- as.data.frame(t(apply(res1_h2, 2, function(x) c(x[2],x[4],x[8]))))
colnames(res2_h2) <- c("coef","se","pval")
res2_h2$hyp <- hypnames[2]
# H2X (Only Appendix)
res1_h2x <- apply(dmod, 2, function(x) coeftest(lm(x~dtmp$g_h2x),vcov.=vcovHC(lm(x~dtmp$g_h2x),type="HC2")))
res2_h2x <- as.data.frame(t(apply(res1_h2x, 2, function(x) c(x[2],x[4],x[8]))))
colnames(res2_h2x) <- c("coef","se","pval")
res2_h2x$hyp <- hypnames[3]
# H3A
res1_h3a <- apply(dmod, 2, function(x) coeftest(lm(x~dtmp$g_h3a),vcov.=vcovHC(lm(x~dtmp$g_h3a),type="HC2")))
res2_h3a <- as.data.frame(t(apply(res1_h3a, 2, function(x) c(x[2],x[4],x[8]))))
colnames(res2_h3a) <- c("coef","se","pval")
res2_h3a$hyp <- hypnames[4]
# H3B
res1_h3b <- apply(dmod, 2, function(x) coeftest(lm(x~dtmp$g_h3b),vcov.=vcovHC(lm(x~dtmp$g_h3b),type="HC2")))
res2_h3b <- as.data.frame(t(apply(res1_h3b, 2, function(x) c(x[2],x[4],x[8]))))
colnames(res2_h3b) <- c("coef","se","pval")
res2_h3b$hyp <- hypnames[5]
# Combine All
res2 <- rbind(res2_h1,res2_h2,res2_h2x,res2_h3a,res2_h3b)
res2$lCI <- res2$coef - qnorm(0.975)*res2$se
res2$uCI <- res2$coef + qnorm(0.975)*res2$se
res2$hyp <- factor(res2$hyp, levels=hypnames)
res_vn <- c("自己申告イデオロギー(DK=0)",
"自己申告イデオロギー(DK=NA)",
"政党支持イデオロギー(DK=0)",
"政党支持イデオロギー(DK=NA)",
"外交安全保障イデオロギー(DK=0)",
"外交安全保障イデオロギー(DK=NA)",
"権利機会平等イデオロギー(DK=0)",
"権利機会平等イデオロギー(DK=NA)",
"政治知識","性別(女性)",
"年齢","居住年数","持ち家",
"教育:小学校/中学校/高校",
"教育:短大/高専/専門学校",
"教育:大卒以上",
"就労","婚姻","子ども")
res3 <- data.frame(Variable = res_vn,
hyp = rep(res2$hyp,2),
stat = c(res2$coef,res2$pval),
lCI = c(res2$lCI,rep(NA,nrow(res2))),
uCI = c(res2$uCI,rep(NA,nrow(res2))),
val = rep(c("実験群と参照群の差(標準化済)","p値(t検定)"),each=nrow(res2)))
res3$Variable <- factor(res3$Variable,levels=rev(res_vn))
res3$val <- factor(res3$val,levels=unique(res3$val))
data2 <- data.frame(val=c("実験群と参照群の差(標準化済)","p値(t検定)"),
vloc1=c(NA,0),
vloc2=c(0,0.1))
data2$val <- factor(data2$val,levels=unique(data2$val))
p <- ggplot(res3, aes(x=Variable,y=stat)) +
geom_errorbar(aes(ymin=lCI,ymax=uCI, color=hyp),width=0.5,
position=position_dodge(width=-0.7)) +
geom_point(aes(shape=hyp, color=hyp),
position=position_dodge(width=-0.7)) +
geom_hline(data=data2, aes(yintercept=vloc1), linetype=1) +
geom_hline(data=data2, aes(yintercept=vloc2), linetype=2) +
#scale_y_continuous(breaks=c(-0.1,0,0.1,0.3,0.6,0.9)) +
scale_shape_discrete(name="比較対象実験群") +
scale_color_discrete(name="比較対象実験群") +
guides(shape=guide_legend(ncol=2,nrow=3,byrow=FALSE),
color=guide_legend(ncol=2,nrow=3,byrow=FALSE)) +
facet_grid(.~val, scales = "free_x") + coord_flip() +
xlab(NULL) + ylab(NULL) +
theme_bw() +
theme(legend.position = "bottom",
plot.margin = margin(t=0.2, b=0.2, r=0.2, l=0.2, "cm"))
p## Warning: position_dodge requires non-overlapping x intervals
## Warning: position_dodge requires non-overlapping x intervals
## Warning: Removed 1 rows containing missing values (geom_hline).
# 統制変数
ctl <- formula( ~ .+ knall + fem + age + lvlen + ownh +
as.factor(edu3) + wk + mar + cld)
# 変数名
vn <- c("(定数項)",
"1.経済成長",
"2.経済成長&貧困削減",
"3.経済成長&学者賛成",
"4.経済成長&貧困&学者",
"政治知識","性別(女性)",
"年齢","居住年数","持ち家",
"教育:短大/高専/専門学校",
"教育:大卒以上",
"就労","婚姻","子ども")
vn_ext <- c(vn[1:3],
"2X.経済成長&格差縮小",
vn[4:15])
vn_idex <- c("イデオロギー",
"イデオロギー×1.成長",
"イデオロギー×2.成長&貧困",
"イデオロギー×3.成長&学者",
"イデオロギー×4.成長&貧困&学者")
vn_idex_ext <- c(vn_idex[1:3],
"イデオロギー×2X.成長&格差",
vn_idex[4:5])
vnx <- c(vn[1:5],vn_idex[1],vn[6:15],
vn_idex[2:5],rep(vn_idex,3))
vnx_ext <- c(vn_ext[1:6],vn_idex_ext[1],vn_ext[7:16],
vn_idex_ext[2:6],rep(vn_idex_ext,3))ftab <- table(dtmp$g_easing_ext, dtmp$check_fail2)
colnames(ftab) <- c("非違反者数","違反者数")
rownames(ftab) <- c("統制群",vn_ext[2:6])
ftab##
## 非違反者数 違反者数
## 統制群 143 41
## 1.経済成長 94 91
## 2.経済成長&貧困削減 37 148
## 2X.経済成長&格差縮小 39 157
## 3.経済成長&学者賛成 43 139
## 4.経済成長&貧困&学者 26 165
print(xtable(ftab, label="mchecktab_2", align = "lll",
caption="実験群ごとのマニピュレーションチェック違反者の分布(違反者は回答が表示された/されていない情報と完全に一致しない被験者)"),
caption.placement="top", file="../out/mchecktab_2.tex")
tmp <- readLines("../out/mchecktab_2.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/mchecktab_2.tex", useBytes = TRUE)ftab <- table(dtmp$g_easing_ext, dtmp$check_fail)
colnames(ftab) <- c("非違反者数","違反者数")
rownames(ftab) <- c("統制群",vn_ext[2:6])
ftab##
## 非違反者数 違反者数
## 統制群 143 41
## 1.経済成長 172 13
## 2.経済成長&貧困削減 169 16
## 2X.経済成長&格差縮小 161 35
## 3.経済成長&学者賛成 172 10
## 4.経済成長&貧困&学者 177 14
print(xtable(ftab, label="mchecktab", align = "lll",
caption="実験群ごとのマニピュレーションチェック違反者の分布(違反者は表示されていない情報を表示されたと回答した被験者)"),
caption.placement="top", file="../out/mchecktab.tex")
tmp <- readLines("../out/mchecktab.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/mchecktab.tex", useBytes = TRUE)m0_1 <- lm(easing_opi ~ as.factor(g_easing_N_ext), data=dtmp)
m0_2 <- lm(update(easing_opi ~ as.factor(g_easing_N_ext),ctl), data=dtmp)
m0_3 <- lm(easing_opi_mis ~ as.factor(g_easing_N_ext), data=dtmp)
m0_4 <- lm(update(easing_opi_mis ~ as.factor(g_easing_N_ext),ctl), data=dtmp)
table_coef(list(m0_1,m0_2,m0_3,m0_4), vcov.est="robust", robust.type="HC2",
single.row=FALSE, custom.variable.names = vn_ext,
m.names = c("基本(DK=0)","拡張(DK=0)",
"基本(DK除外)","拡張(DK除外)"),
dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
caption="情報環境刺激による金融緩和の説明(重回帰分析)",
custom.footnote = "最小二乗法による重回帰分析.ロバスト標準誤差(HC2)使用.",
label="basetab",
format = "tex", file.name = "../out/v6_basetab")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT as.factor(g_easing_N_ext)1 1.経済成長
## KEPT as.factor(g_easing_N_ext)2 2.経済成長&貧困削減
## KEPT as.factor(g_easing_N_ext)3 2X.経済成長&格差縮小
## KEPT as.factor(g_easing_N_ext)4 3.経済成長&学者賛成
## KEPT as.factor(g_easing_N_ext)5 4.経済成長&貧困&学者
## KEPT knall 政治知識
## KEPT fem 性別(女性)
## KEPT age 年齢
## KEPT lvlen 居住年数
## KEPT ownh 持ち家
## KEPT as.factor(edu3)1 教育:短大/高専/専門学校
## KEPT as.factor(edu3)2 教育:大卒以上
## KEPT wk 就労
## KEPT mar 婚姻
## KEPT cld 子ども
## The table was written to the file '../out/v6_basetab.tex'.
##
## =====================================================================
## 基本(DK=0) 拡張(DK=0) 基本(DK除外) 拡張(DK除外)
## ---------------------------------------------------------------------
## (定数項) 0.788 *** 1.131 *** 1.007 *** 1.435 ***
## (0.089) (0.204) (0.106) (0.230)
## 1.経済成長 0.115 0.130 -0.007 0.017
## (0.132) (0.129) (0.150) (0.148)
## 2.経済成長&貧困削減 0.298 * 0.287 * 0.182 0.185
## (0.129) (0.127) (0.145) (0.143)
## 2X.経済成長&格差縮小 0.100 0.077 -0.007 -0.017
## (0.122) (0.121) (0.140) (0.139)
## 3.経済成長&学者賛成 0.141 0.137 -0.013 -0.006
## (0.125) (0.122) (0.140) (0.138)
## 4.経済成長&貧困&学者 0.348 ** 0.368 ** 0.226 0.257 +
## (0.122) (0.120) (0.138) (0.137)
## 政治知識 0.235 0.056
## (0.143) (0.158)
## 性別(女性) -0.317 *** -0.305 ***
## (0.079) (0.086)
## 年齢 -0.008 * -0.008 *
## (0.004) (0.004)
## 居住年数 -0.058 + -0.053
## (0.030) (0.033)
## 持ち家 -0.002 0.002
## (0.082) (0.089)
## 教育:短大/高専/専門学校 0.145 0.126
## (0.124) (0.134)
## 教育:大卒以上 0.102 0.116
## (0.101) (0.113)
## 就労 -0.005 -0.018
## (0.082) (0.090)
## 婚姻 -0.106 -0.131
## (0.111) (0.120)
## 子ども 0.222 * 0.234 +
## (0.112) (0.120)
## ---------------------------------------------------------------------
## R^2 0.010 0.045 0.007 0.036
## Adj. R^2 0.005 0.032 0.002 0.021
## Num. obs. 1123 1123 1000 1000
## RMSE 1.220 1.203 1.245 1.233
## =====================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.ロバスト標準誤差(HC2)使用.
tmp <- readLines("../out/v6_basetab.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_basetab.tex", useBytes = TRUE)# Main Models (With Different Base Category)
m1b0_1 <- lm(easing_opi ~ as.factor(g_easing_N)*ide_self, data=dtmp)
m1b0_2 <- lm(update(easing_opi ~ as.factor(g_easing_N)*ide_self,ctl), data=dtmp)
m1b1_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_self, data=dtmp)
m1b1_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_self,ctl), data=dtmp)
m1b2_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_self, data=dtmp)
m1b2_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_self,ctl), data=dtmp)
m1b3_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_self, data=dtmp)
m1b3_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_self,ctl), data=dtmp)
m1b4_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_self, data=dtmp)
m1b4_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_self,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
m1mg_1 <- data.frame(
tr = seq(0,4), ide="自己申告",
rbind(coeftest(m1b0_1, vcovHC(m1b0_1, "HC2"))[6,],
coeftest(m1b1_1, vcovHC(m1b1_1, "HC2"))[6,],
coeftest(m1b2_1, vcovHC(m1b2_1, "HC2"))[6,],
coeftest(m1b3_1, vcovHC(m1b3_1, "HC2"))[6,],
coeftest(m1b4_1, vcovHC(m1b4_1, "HC2"))[6,])
)
names(m1mg_1)[3:6] <- c("est","se","tval","pval")
m1mg_2 <- data.frame(
tr = seq(0,4), ide="自己申告",
rbind(coeftest(m1b0_2, vcovHC(m1b0_2, "HC2"))[6,],
coeftest(m1b1_2, vcovHC(m1b1_2, "HC2"))[6,],
coeftest(m1b2_2, vcovHC(m1b2_2, "HC2"))[6,],
coeftest(m1b3_2, vcovHC(m1b3_2, "HC2"))[6,],
coeftest(m1b4_2, vcovHC(m1b4_2, "HC2"))[6,])
)
names(m1mg_2)[3:6] <- c("est","se","tval","pval")
# Models with Missing Values as NA
nm1b0_1 <- lm(easing_opi_mis ~ as.factor(g_easing_N)*ide_self_mis, data=dtmp)
nm1b0_2 <- lm(update(easing_opi_mis ~ as.factor(g_easing_N)*ide_self_mis,ctl), data=dtmp)
nm1b1_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_self_mis, data=dtmp)
nm1b1_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_self_mis,ctl), data=dtmp)
nm1b2_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_self_mis, data=dtmp)
nm1b2_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_self_mis,ctl), data=dtmp)
nm1b3_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_self_mis, data=dtmp)
nm1b3_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_self_mis,ctl), data=dtmp)
nm1b4_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_self_mis, data=dtmp)
nm1b4_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_self_mis,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
nm1mg_1 <- data.frame(
tr = seq(0,4), ide="自己申告",
rbind(coeftest(nm1b0_1, vcovHC(nm1b0_1, "HC2"))[6,],
coeftest(nm1b1_1, vcovHC(nm1b1_1, "HC2"))[6,],
coeftest(nm1b2_1, vcovHC(nm1b2_1, "HC2"))[6,],
coeftest(nm1b3_1, vcovHC(nm1b3_1, "HC2"))[6,],
coeftest(nm1b4_1, vcovHC(nm1b4_1, "HC2"))[6,])
)
names(nm1mg_1)[3:6] <- c("est","se","tval","pval")
nm1mg_2 <- data.frame(
tr = seq(0,4), ide="自己申告",
rbind(coeftest(nm1b0_2, vcovHC(nm1b0_2, "HC2"))[6,],
coeftest(nm1b1_2, vcovHC(nm1b1_2, "HC2"))[6,],
coeftest(nm1b2_2, vcovHC(nm1b2_2, "HC2"))[6,],
coeftest(nm1b3_2, vcovHC(nm1b3_2, "HC2"))[6,],
coeftest(nm1b4_2, vcovHC(nm1b4_2, "HC2"))[6,])
)
names(nm1mg_2)[3:6] <- c("est","se","tval","pval")
# Models without Respondents Failing Manipulation Check
fm1b0_1 <- lm(easing_opi ~ as.factor(g_easing_N)*ide_self, data=dtmp[which(dtmp$check_fail==0),])
fm1b0_2 <- lm(update(easing_opi ~ as.factor(g_easing_N)*ide_self,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm1b1_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_self, data=dtmp[which(dtmp$check_fail==0),])
fm1b1_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_self,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm1b2_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_self, data=dtmp[which(dtmp$check_fail==0),])
fm1b2_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_self,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm1b3_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_self, data=dtmp[which(dtmp$check_fail==0),])
fm1b3_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_self,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm1b4_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_self, data=dtmp[which(dtmp$check_fail==0),])
fm1b4_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_self,ctl), data=dtmp[which(dtmp$check_fail==0),])
## Marginal Effect of Ideology on Easing Opinion
fm1mg_1 <- data.frame(
tr = seq(0,4), ide="自己申告",
rbind(coeftest(fm1b0_1, vcovHC(fm1b0_1, "HC2"))[6,],
coeftest(fm1b1_1, vcovHC(fm1b1_1, "HC2"))[6,],
coeftest(fm1b2_1, vcovHC(fm1b2_1, "HC2"))[6,],
coeftest(fm1b3_1, vcovHC(fm1b3_1, "HC2"))[6,],
coeftest(fm1b4_1, vcovHC(fm1b4_1, "HC2"))[6,])
)
names(fm1mg_1)[3:6] <- c("est","se","tval","pval")
fm1mg_2 <- data.frame(
tr = seq(0,4), ide="自己申告",
rbind(coeftest(fm1b0_2, vcovHC(fm1b0_2, "HC2"))[6,],
coeftest(fm1b1_2, vcovHC(fm1b1_2, "HC2"))[6,],
coeftest(fm1b2_2, vcovHC(fm1b2_2, "HC2"))[6,],
coeftest(fm1b3_2, vcovHC(fm1b3_2, "HC2"))[6,],
coeftest(fm1b4_2, vcovHC(fm1b4_2, "HC2"))[6,])
)
names(fm1mg_2)[3:6] <- c("est","se","tval","pval")# Main Models (With Different Base Category)
m2b0_1 <- lm(easing_opi ~ as.factor(g_easing_N)*ide_psup, data=dtmp)
m2b0_2 <- lm(update(easing_opi ~ as.factor(g_easing_N)*ide_psup,ctl), data=dtmp)
m2b1_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_psup, data=dtmp)
m2b1_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_psup,ctl), data=dtmp)
m2b2_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_psup, data=dtmp)
m2b2_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_psup,ctl), data=dtmp)
m2b3_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_psup, data=dtmp)
m2b3_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_psup,ctl), data=dtmp)
m2b4_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_psup, data=dtmp)
m2b4_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_psup,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
m2mg_1 <- data.frame(
tr = seq(0,4), ide="政党支持",
rbind(coeftest(m2b0_1, vcovHC(m2b0_1, "HC2"))[6,],
coeftest(m2b1_1, vcovHC(m2b1_1, "HC2"))[6,],
coeftest(m2b2_1, vcovHC(m2b2_1, "HC2"))[6,],
coeftest(m2b3_1, vcovHC(m2b3_1, "HC2"))[6,],
coeftest(m2b4_1, vcovHC(m2b4_1, "HC2"))[6,])
)
names(m2mg_1)[3:6] <- c("est","se","tval","pval")
m2mg_2 <- data.frame(
tr = seq(0,4), ide="政党支持",
rbind(coeftest(m2b0_2, vcovHC(m2b0_2, "HC2"))[6,],
coeftest(m2b1_2, vcovHC(m2b1_2, "HC2"))[6,],
coeftest(m2b2_2, vcovHC(m2b2_2, "HC2"))[6,],
coeftest(m2b3_2, vcovHC(m2b3_2, "HC2"))[6,],
coeftest(m2b4_2, vcovHC(m2b4_2, "HC2"))[6,])
)
names(m2mg_2)[3:6] <- c("est","se","tval","pval")
# Models with Missing Values as NA
nm2b0_1 <- lm(easing_opi_mis ~ as.factor(g_easing_N)*ide_psup_mis, data=dtmp)
nm2b0_2 <- lm(update(easing_opi_mis ~ as.factor(g_easing_N)*ide_psup_mis,ctl), data=dtmp)
nm2b1_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_psup_mis, data=dtmp)
nm2b1_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_psup_mis,ctl), data=dtmp)
nm2b2_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_psup_mis, data=dtmp)
nm2b2_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_psup_mis,ctl), data=dtmp)
nm2b3_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_psup_mis, data=dtmp)
nm2b3_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_psup_mis,ctl), data=dtmp)
nm2b4_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_psup_mis, data=dtmp)
nm2b4_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_psup_mis,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
nm2mg_1 <- data.frame(
tr = seq(0,4), ide="政党支持",
rbind(coeftest(nm2b0_1, vcovHC(nm2b0_1, "HC2"))[6,],
coeftest(nm2b1_1, vcovHC(nm2b1_1, "HC2"))[6,],
coeftest(nm2b2_1, vcovHC(nm2b2_1, "HC2"))[6,],
coeftest(nm2b3_1, vcovHC(nm2b3_1, "HC2"))[6,],
coeftest(nm2b4_1, vcovHC(nm2b4_1, "HC2"))[6,])
)
names(nm2mg_1)[3:6] <- c("est","se","tval","pval")
nm2mg_2 <- data.frame(
tr = seq(0,4), ide="政党支持",
rbind(coeftest(nm2b0_2, vcovHC(nm2b0_2, "HC2"))[6,],
coeftest(nm2b1_2, vcovHC(nm2b1_2, "HC2"))[6,],
coeftest(nm2b2_2, vcovHC(nm2b2_2, "HC2"))[6,],
coeftest(nm2b3_2, vcovHC(nm2b3_2, "HC2"))[6,],
coeftest(nm2b4_2, vcovHC(nm2b4_2, "HC2"))[6,])
)
names(nm2mg_2)[3:6] <- c("est","se","tval","pval")
# Models without Respondents Failing Manipulation Check
fm2b0_1 <- lm(easing_opi ~ as.factor(g_easing_N)*ide_psup, data=dtmp[which(dtmp$check_fail==0),])
fm2b0_2 <- lm(update(easing_opi ~ as.factor(g_easing_N)*ide_psup,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm2b1_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_psup, data=dtmp[which(dtmp$check_fail==0),])
fm2b1_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_psup,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm2b2_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_psup, data=dtmp[which(dtmp$check_fail==0),])
fm2b2_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_psup,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm2b3_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_psup, data=dtmp[which(dtmp$check_fail==0),])
fm2b3_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_psup,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm2b4_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_psup, data=dtmp[which(dtmp$check_fail==0),])
fm2b4_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_psup,ctl), data=dtmp[which(dtmp$check_fail==0),])
## Marginal Effect of Ideology on Easing Opinion
fm2mg_1 <- data.frame(
tr = seq(0,4), ide="政党支持",
rbind(coeftest(fm2b0_1, vcovHC(fm2b0_1, "HC2"))[6,],
coeftest(fm2b1_1, vcovHC(fm2b1_1, "HC2"))[6,],
coeftest(fm2b2_1, vcovHC(fm2b2_1, "HC2"))[6,],
coeftest(fm2b3_1, vcovHC(fm2b3_1, "HC2"))[6,],
coeftest(fm2b4_1, vcovHC(fm2b4_1, "HC2"))[6,])
)
names(fm2mg_1)[3:6] <- c("est","se","tval","pval")
fm2mg_2 <- data.frame(
tr = seq(0,4), ide="政党支持",
rbind(coeftest(fm2b0_2, vcovHC(fm2b0_2, "HC2"))[6,],
coeftest(fm2b1_2, vcovHC(fm2b1_2, "HC2"))[6,],
coeftest(fm2b2_2, vcovHC(fm2b2_2, "HC2"))[6,],
coeftest(fm2b3_2, vcovHC(fm2b3_2, "HC2"))[6,],
coeftest(fm2b4_2, vcovHC(fm2b4_2, "HC2"))[6,])
)
names(fm2mg_2)[3:6] <- c("est","se","tval","pval")# Main Models (With Different Base Category)
m3b0_1 <- lm(easing_opi ~ as.factor(g_easing_N)*ide_iss_1, data=dtmp)
m3b0_2 <- lm(update(easing_opi ~ as.factor(g_easing_N)*ide_iss_1,ctl), data=dtmp)
m3b1_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_iss_1, data=dtmp)
m3b1_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_iss_1,ctl), data=dtmp)
m3b2_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_iss_1, data=dtmp)
m3b2_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_iss_1,ctl), data=dtmp)
m3b3_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_iss_1, data=dtmp)
m3b3_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_iss_1,ctl), data=dtmp)
m3b4_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_iss_1, data=dtmp)
m3b4_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_iss_1,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
m3mg_1 <- data.frame(
tr = seq(0,4), ide="外交安全保障",
rbind(coeftest(m3b0_1, vcovHC(m3b0_1, "HC2"))[6,],
coeftest(m3b1_1, vcovHC(m3b1_1, "HC2"))[6,],
coeftest(m3b2_1, vcovHC(m3b2_1, "HC2"))[6,],
coeftest(m3b3_1, vcovHC(m3b3_1, "HC2"))[6,],
coeftest(m3b4_1, vcovHC(m3b4_1, "HC2"))[6,])
)
names(m3mg_1)[3:6] <- c("est","se","tval","pval")
m3mg_2 <- data.frame(
tr = seq(0,4), ide="外交安全保障",
rbind(coeftest(m3b0_2, vcovHC(m3b0_2, "HC2"))[6,],
coeftest(m3b1_2, vcovHC(m3b1_2, "HC2"))[6,],
coeftest(m3b2_2, vcovHC(m3b2_2, "HC2"))[6,],
coeftest(m3b3_2, vcovHC(m3b3_2, "HC2"))[6,],
coeftest(m3b4_2, vcovHC(m3b4_2, "HC2"))[6,])
)
names(m3mg_2)[3:6] <- c("est","se","tval","pval")
# Models with Missing Values as NA
nm3b0_1 <- lm(easing_opi_mis ~ as.factor(g_easing_N)*ide_iss_mis_1, data=dtmp)
nm3b0_2 <- lm(update(easing_opi_mis ~ as.factor(g_easing_N)*ide_iss_mis_1,ctl), data=dtmp)
nm3b1_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_iss_mis_1, data=dtmp)
nm3b1_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_iss_mis_1,ctl), data=dtmp)
nm3b2_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_iss_mis_1, data=dtmp)
nm3b2_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_iss_mis_1,ctl), data=dtmp)
nm3b3_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_iss_mis_1, data=dtmp)
nm3b3_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_iss_mis_1,ctl), data=dtmp)
nm3b4_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_iss_mis_1, data=dtmp)
nm3b4_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_iss_mis_1,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
nm3mg_1 <- data.frame(
tr = seq(0,4), ide="外交安全保障",
rbind(coeftest(nm3b0_1, vcovHC(nm3b0_1, "HC2"))[6,],
coeftest(nm3b1_1, vcovHC(nm3b1_1, "HC2"))[6,],
coeftest(nm3b2_1, vcovHC(nm3b2_1, "HC2"))[6,],
coeftest(nm3b3_1, vcovHC(nm3b3_1, "HC2"))[6,],
coeftest(nm3b4_1, vcovHC(nm3b4_1, "HC2"))[6,])
)
names(nm3mg_1)[3:6] <- c("est","se","tval","pval")
nm3mg_2 <- data.frame(
tr = seq(0,4), ide="外交安全保障",
rbind(coeftest(nm3b0_2, vcovHC(nm3b0_2, "HC2"))[6,],
coeftest(nm3b1_2, vcovHC(nm3b1_2, "HC2"))[6,],
coeftest(nm3b2_2, vcovHC(nm3b2_2, "HC2"))[6,],
coeftest(nm3b3_2, vcovHC(nm3b3_2, "HC2"))[6,],
coeftest(nm3b4_2, vcovHC(nm3b4_2, "HC2"))[6,])
)
names(nm3mg_2)[3:6] <- c("est","se","tval","pval")
# Models without Respondents Failing Manipulation Check
fm3b0_1 <- lm(easing_opi ~ as.factor(g_easing_N)*ide_iss_1, data=dtmp[which(dtmp$check_fail==0),])
fm3b0_2 <- lm(update(easing_opi ~ as.factor(g_easing_N)*ide_iss_1,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm3b1_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_iss_1, data=dtmp[which(dtmp$check_fail==0),])
fm3b1_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_iss_1,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm3b2_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_iss_1, data=dtmp[which(dtmp$check_fail==0),])
fm3b2_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_iss_1,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm3b3_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_iss_1, data=dtmp[which(dtmp$check_fail==0),])
fm3b3_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_iss_1,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm3b4_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_iss_1, data=dtmp[which(dtmp$check_fail==0),])
fm3b4_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_iss_1,ctl), data=dtmp[which(dtmp$check_fail==0),])
## Marginal Effect of Ideology on Easing Opinion
fm3mg_1 <- data.frame(
tr = seq(0,4), ide="外交安全保障",
rbind(coeftest(fm3b0_1, vcovHC(fm3b0_1, "HC2"))[6,],
coeftest(fm3b1_1, vcovHC(fm3b1_1, "HC2"))[6,],
coeftest(fm3b2_1, vcovHC(fm3b2_1, "HC2"))[6,],
coeftest(fm3b3_1, vcovHC(fm3b3_1, "HC2"))[6,],
coeftest(fm3b4_1, vcovHC(fm3b4_1, "HC2"))[6,])
)
names(fm3mg_1)[3:6] <- c("est","se","tval","pval")
fm3mg_2 <- data.frame(
tr = seq(0,4), ide="外交安全保障",
rbind(coeftest(fm3b0_2, vcovHC(fm3b0_2, "HC2"))[6,],
coeftest(fm3b1_2, vcovHC(fm3b1_2, "HC2"))[6,],
coeftest(fm3b2_2, vcovHC(fm3b2_2, "HC2"))[6,],
coeftest(fm3b3_2, vcovHC(fm3b3_2, "HC2"))[6,],
coeftest(fm3b4_2, vcovHC(fm3b4_2, "HC2"))[6,])
)
names(fm3mg_2)[3:6] <- c("est","se","tval","pval")# Main Models (With Different Base Category)
m4b0_1 <- lm(easing_opi ~ as.factor(g_easing_N)*ide_iss_2, data=dtmp)
m4b0_2 <- lm(update(easing_opi ~ as.factor(g_easing_N)*ide_iss_2,ctl), data=dtmp)
m4b1_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_iss_2, data=dtmp)
m4b1_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_iss_2,ctl), data=dtmp)
m4b2_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_iss_2, data=dtmp)
m4b2_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_iss_2,ctl), data=dtmp)
m4b3_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_iss_2, data=dtmp)
m4b3_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_iss_2,ctl), data=dtmp)
m4b4_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_iss_2, data=dtmp)
m4b4_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_iss_2,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
m4mg_1 <- data.frame(
tr = seq(0,4), ide="権利機会平等",
rbind(coeftest(m4b0_1, vcovHC(m4b0_1, "HC2"))[6,],
coeftest(m4b1_1, vcovHC(m4b1_1, "HC2"))[6,],
coeftest(m4b2_1, vcovHC(m4b2_1, "HC2"))[6,],
coeftest(m4b3_1, vcovHC(m4b3_1, "HC2"))[6,],
coeftest(m4b4_1, vcovHC(m4b4_1, "HC2"))[6,])
)
names(m4mg_1)[3:6] <- c("est","se","tval","pval")
m4mg_2 <- data.frame(
tr = seq(0,4), ide="権利機会平等",
rbind(coeftest(m4b0_2, vcovHC(m4b0_2, "HC2"))[6,],
coeftest(m4b1_2, vcovHC(m4b1_2, "HC2"))[6,],
coeftest(m4b2_2, vcovHC(m4b2_2, "HC2"))[6,],
coeftest(m4b3_2, vcovHC(m4b3_2, "HC2"))[6,],
coeftest(m4b4_2, vcovHC(m4b4_2, "HC2"))[6,])
)
names(m4mg_2)[3:6] <- c("est","se","tval","pval")
# Models with Missing Values as NA
nm4b0_1 <- lm(easing_opi_mis ~ as.factor(g_easing_N)*ide_iss_mis_2, data=dtmp)
nm4b0_2 <- lm(update(easing_opi_mis ~ as.factor(g_easing_N)*ide_iss_mis_2,ctl), data=dtmp)
nm4b1_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_iss_mis_2, data=dtmp)
nm4b1_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_iss_mis_2,ctl), data=dtmp)
nm4b2_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_iss_mis_2, data=dtmp)
nm4b2_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_iss_mis_2,ctl), data=dtmp)
nm4b3_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_iss_mis_2, data=dtmp)
nm4b3_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_iss_mis_2,ctl), data=dtmp)
nm4b4_1 <- lm(easing_opi_mis ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_iss_mis_2, data=dtmp)
nm4b4_2 <- lm(update(easing_opi_mis ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_iss_mis_2,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
nm4mg_1 <- data.frame(
tr = seq(0,4), ide="権利機会平等",
rbind(coeftest(nm4b0_1, vcovHC(nm4b0_1, "HC2"))[6,],
coeftest(nm4b1_1, vcovHC(nm4b1_1, "HC2"))[6,],
coeftest(nm4b2_1, vcovHC(nm4b2_1, "HC2"))[6,],
coeftest(nm4b3_1, vcovHC(nm4b3_1, "HC2"))[6,],
coeftest(nm4b4_1, vcovHC(nm4b4_1, "HC2"))[6,])
)
names(nm4mg_1)[3:6] <- c("est","se","tval","pval")
nm4mg_2 <- data.frame(
tr = seq(0,4), ide="権利機会平等",
rbind(coeftest(nm4b0_2, vcovHC(nm4b0_2, "HC2"))[6,],
coeftest(nm4b1_2, vcovHC(nm4b1_2, "HC2"))[6,],
coeftest(nm4b2_2, vcovHC(nm4b2_2, "HC2"))[6,],
coeftest(nm4b3_2, vcovHC(nm4b3_2, "HC2"))[6,],
coeftest(nm4b4_2, vcovHC(nm4b4_2, "HC2"))[6,])
)
names(nm4mg_2)[3:6] <- c("est","se","tval","pval")
# Models without Respondents Failing Manipulation Check
fm4b0_1 <- lm(easing_opi ~ as.factor(g_easing_N)*ide_iss_2, data=dtmp[which(dtmp$check_fail==0),])
fm4b0_2 <- lm(update(easing_opi ~ as.factor(g_easing_N)*ide_iss_2,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm4b1_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_iss_2, data=dtmp[which(dtmp$check_fail==0),])
fm4b1_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("1","0","2","3","4"))*ide_iss_2,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm4b2_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_iss_2, data=dtmp[which(dtmp$check_fail==0),])
fm4b2_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("2","0","1","3","4"))*ide_iss_2,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm4b3_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_iss_2, data=dtmp[which(dtmp$check_fail==0),])
fm4b3_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("3","0","1","2","4"))*ide_iss_2,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm4b4_1 <- lm(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_iss_2, data=dtmp[which(dtmp$check_fail==0),])
fm4b4_2 <- lm(update(easing_opi ~ factor(g_easing_N,levels=c("4","0","1","2","3"))*ide_iss_2,ctl), data=dtmp[which(dtmp$check_fail==0),])
## Marginal Effect of Ideology on Easing Opinion
fm4mg_1 <- data.frame(
tr = seq(0,4), ide="権利機会平等",
rbind(coeftest(fm4b0_1, vcovHC(fm4b0_1, "HC2"))[6,],
coeftest(fm4b1_1, vcovHC(fm4b1_1, "HC2"))[6,],
coeftest(fm4b2_1, vcovHC(fm4b2_1, "HC2"))[6,],
coeftest(fm4b3_1, vcovHC(fm4b3_1, "HC2"))[6,],
coeftest(fm4b4_1, vcovHC(fm4b4_1, "HC2"))[6,])
)
names(fm4mg_1)[3:6] <- c("est","se","tval","pval")
fm4mg_2 <- data.frame(
tr = seq(0,4), ide="権利機会平等",
rbind(coeftest(fm4b0_2, vcovHC(fm4b0_2, "HC2"))[6,],
coeftest(fm4b1_2, vcovHC(fm4b1_2, "HC2"))[6,],
coeftest(fm4b2_2, vcovHC(fm4b2_2, "HC2"))[6,],
coeftest(fm4b3_2, vcovHC(fm4b3_2, "HC2"))[6,],
coeftest(fm4b4_2, vcovHC(fm4b4_2, "HC2"))[6,])
)
names(fm4mg_2)[3:6] <- c("est","se","tval","pval")# Combine Outputs
mgdt_1 <- rbind(m1mg_1,m2mg_1,m3mg_1,m4mg_1)
mgdt_2 <- rbind(m1mg_2,m2mg_2,m3mg_2,m4mg_2)
nmgdt_1 <- rbind(nm1mg_1,nm2mg_1,nm3mg_1,nm4mg_1)
nmgdt_2 <- rbind(nm1mg_2,nm2mg_2,nm3mg_2,nm4mg_2)
fmgdt_1 <- rbind(fm1mg_1,fm2mg_1,fm3mg_1,fm4mg_1)
fmgdt_2 <- rbind(fm1mg_2,fm2mg_2,fm3mg_2,fm4mg_2)
# 95% Confidence Intervals
mgdt_1$lci95 <- mgdt_1$est - qnorm(0.975)*mgdt_1$se
mgdt_1$uci95 <- mgdt_1$est + qnorm(0.975)*mgdt_1$se
mgdt_2$lci95 <- mgdt_2$est - qnorm(0.975)*mgdt_2$se
mgdt_2$uci95 <- mgdt_2$est + qnorm(0.975)*mgdt_2$se
nmgdt_1$lci95 <- nmgdt_1$est - qnorm(0.975)*nmgdt_1$se
nmgdt_1$uci95 <- nmgdt_1$est + qnorm(0.975)*nmgdt_1$se
nmgdt_2$lci95 <- nmgdt_2$est - qnorm(0.975)*nmgdt_2$se
nmgdt_2$uci95 <- nmgdt_2$est + qnorm(0.975)*nmgdt_2$se
fmgdt_1$lci95 <- fmgdt_1$est - qnorm(0.975)*fmgdt_1$se
fmgdt_1$uci95 <- fmgdt_1$est + qnorm(0.975)*fmgdt_1$se
fmgdt_2$lci95 <- fmgdt_2$est - qnorm(0.975)*fmgdt_2$se
fmgdt_2$uci95 <- fmgdt_2$est + qnorm(0.975)*fmgdt_2$se
# 90% Confidence Intervals
mgdt_1$lci90 <- mgdt_1$est - qnorm(0.95)*mgdt_1$se
mgdt_1$uci90 <- mgdt_1$est + qnorm(0.95)*mgdt_1$se
mgdt_2$lci90 <- mgdt_2$est - qnorm(0.95)*mgdt_2$se
mgdt_2$uci90 <- mgdt_2$est + qnorm(0.95)*mgdt_2$se
nmgdt_1$lci90 <- nmgdt_1$est - qnorm(0.95)*nmgdt_1$se
nmgdt_1$uci90 <- nmgdt_1$est + qnorm(0.95)*nmgdt_1$se
nmgdt_2$lci90 <- nmgdt_2$est - qnorm(0.95)*nmgdt_2$se
nmgdt_2$uci90 <- nmgdt_2$est + qnorm(0.95)*nmgdt_2$se
fmgdt_1$lci90 <- fmgdt_1$est - qnorm(0.95)*fmgdt_1$se
fmgdt_1$uci90 <- fmgdt_1$est + qnorm(0.95)*fmgdt_1$se
fmgdt_2$lci90 <- fmgdt_2$est - qnorm(0.95)*fmgdt_2$se
fmgdt_2$uci90 <- fmgdt_2$est + qnorm(0.95)*fmgdt_2$se
# P-test Thresholds
mgdt_1$ptest <- ifelse(mgdt_1$pval<0.05,"p<.05",
ifelse(mgdt_1$pval<0.1,"p<.10","n.s.(p>=.10)"))
mgdt_1$ptest <- factor(mgdt_1$ptest,
levels=c("p<.05","p<.10","n.s.(p>=.10)"))
mgdt_2$ptest <- ifelse(mgdt_2$pval<0.05,"p<.05",
ifelse(mgdt_2$pval<0.1,"p<.10","n.s.(p>=.10)"))
mgdt_2$ptest <- factor(mgdt_2$ptest,
levels=c("p<.05","p<.10","n.s.(p>=.10)"))
nmgdt_1$ptest <- ifelse(nmgdt_1$pval<0.05,"p<.05",
ifelse(nmgdt_1$pval<0.1,"p<.10","n.s.(p>=.10)"))
nmgdt_1$ptest <- factor(nmgdt_1$ptest,
levels=c("p<.05","p<.10","n.s.(p>=.10)"))
nmgdt_2$ptest <- ifelse(nmgdt_2$pval<0.05,"p<.05",
ifelse(nmgdt_2$pval<0.1,"p<.10","n.s.(p>=.10)"))
nmgdt_2$ptest <- factor(nmgdt_2$ptest,
levels=c("p<.05","p<.10","n.s.(p>=.10)"))
fmgdt_1$ptest <- ifelse(fmgdt_1$pval<0.05,"p<.05",
ifelse(fmgdt_1$pval<0.1,"p<.10","n.s.(p>=.10)"))
fmgdt_1$ptest <- factor(fmgdt_1$ptest,
levels=c("p<.05","p<.10","n.s.(p>=.10)"))
fmgdt_2$ptest <- ifelse(fmgdt_2$pval<0.05,"p<.05",
ifelse(fmgdt_2$pval<0.1,"p<.10","n.s.(p>=.10)"))
fmgdt_2$ptest <- factor(fmgdt_2$ptest,
levels=c("p<.05","p<.10","n.s.(p>=.10)"))
# Make Other Variables Factor
mgdt_1$tr <- factor(mgdt_1$tr, levels=unique(mgdt_1$tr))
mgdt_1$ide <- factor(mgdt_1$ide, levels=unique(mgdt_1$ide))
mgdt_2$tr <- factor(mgdt_2$tr, levels=unique(mgdt_2$tr))
mgdt_2$ide <- factor(mgdt_2$ide, levels=unique(mgdt_2$ide))
nmgdt_1$tr <- factor(nmgdt_1$tr, levels=unique(nmgdt_1$tr))
nmgdt_1$ide <- factor(nmgdt_1$ide, levels=unique(nmgdt_1$ide))
nmgdt_2$tr <- factor(nmgdt_2$tr, levels=unique(nmgdt_2$tr))
nmgdt_2$ide <- factor(nmgdt_2$ide, levels=unique(nmgdt_2$ide))
fmgdt_1$tr <- factor(fmgdt_1$tr, levels=unique(fmgdt_1$tr))
fmgdt_1$ide <- factor(fmgdt_1$ide, levels=unique(fmgdt_1$ide))
fmgdt_2$tr <- factor(fmgdt_2$tr, levels=unique(fmgdt_2$tr))
fmgdt_2$ide <- factor(fmgdt_2$ide, levels=unique(fmgdt_2$ide))exportsimplot <- function(dt, captiontxt) {
p <- ggplot(dt, aes_string(x="tr", y="est")) +
geom_hline(aes(yintercept=0), linetype=2) +
geom_point(aes_string(color="ptest", shape="ptest"), size=2) +
geom_errorbar(aes_string(ymin="lci95", ymax="uci95", color="ptest"), width=0.3) +
geom_errorbar(aes_string(ymin="lci90", ymax="uci90", color="ptest"), width=0, size=1) +
facet_grid(~ide) +
scale_color_manual(name="", values=c("red2","darkorange2","gray50"),
drop = FALSE) +
scale_shape_discrete(name="", drop = FALSE) +
ylab("イデオロギーの条件付き係数+95%信頼区間\n(太線は90%信頼区間)") +
xlab("実験群\n0:統制群; 1.経済成長; 2.経済成長&貧困削減\n3.経済成長&学者賛成; 4.経済成長&貧困削減&学者賛成") +
labs(subtitle="イデオロギー指標",
caption=captiontxt) +
theme_bw() +
theme(plot.margin = unit(c(0.5,0.5,0.5,0.5), "cm"),
panel.grid = element_line(color=NA),
plot.subtitle = element_text(hjust=0.5),
axis.text.x = element_text(color="black", size=11),
axis.text.y = element_text(color="black"),
legend.position = "bottom")
p
} ggsave("../out/v6_expres_2.png", p, width=8, height=5)
# ggsave("../out/v6_expres_2.pdf", p, width=8, height=5, family = "Japan1")# H1
h1cf <- rbind(coeftest(m1b0_1, vcovHC(m1b0_1, "HC2"))[7,],
coeftest(m2b0_1, vcovHC(m2b0_1, "HC2"))[7,],
coeftest(m3b0_1, vcovHC(m3b0_1, "HC2"))[7,],
coeftest(m4b0_1, vcovHC(m4b0_1, "HC2"))[7,],
coeftest(m1b0_2, vcovHC(m1b0_2, "HC2"))[17,],
coeftest(m2b0_2, vcovHC(m2b0_2, "HC2"))[17,],
coeftest(m3b0_2, vcovHC(m3b0_2, "HC2"))[17,],
coeftest(m4b0_2, vcovHC(m4b0_2, "HC2"))[17,],
coeftest(nm1b0_1, vcovHC(nm1b0_1, "HC2"))[7,],
coeftest(nm2b0_1, vcovHC(nm2b0_1, "HC2"))[7,],
coeftest(nm3b0_1, vcovHC(nm3b0_1, "HC2"))[7,],
coeftest(nm4b0_1, vcovHC(nm4b0_1, "HC2"))[7,],
coeftest(nm1b0_2, vcovHC(nm1b0_2, "HC2"))[17,],
coeftest(nm2b0_2, vcovHC(nm2b0_2, "HC2"))[17,],
coeftest(nm3b0_2, vcovHC(nm3b0_2, "HC2"))[17,],
coeftest(nm4b0_2, vcovHC(nm4b0_2, "HC2"))[17,],
coeftest(fm1b0_1, vcovHC(fm1b0_1, "HC2"))[7,],
coeftest(fm2b0_1, vcovHC(fm2b0_1, "HC2"))[7,],
coeftest(fm3b0_1, vcovHC(fm3b0_1, "HC2"))[7,],
coeftest(fm4b0_1, vcovHC(fm4b0_1, "HC2"))[7,],
coeftest(fm1b0_2, vcovHC(fm1b0_2, "HC2"))[17,],
coeftest(fm2b0_2, vcovHC(fm2b0_2, "HC2"))[17,],
coeftest(fm3b0_2, vcovHC(fm3b0_2, "HC2"))[17,],
coeftest(fm4b0_2, vcovHC(fm4b0_2, "HC2"))[17,])
# H2
h2cf <- rbind(coeftest(m1b1_1, vcovHC(m1b1_1, "HC2"))[8,],
coeftest(m2b1_1, vcovHC(m2b1_1, "HC2"))[8,],
coeftest(m3b1_1, vcovHC(m3b1_1, "HC2"))[8,],
coeftest(m4b1_1, vcovHC(m4b1_1, "HC2"))[8,],
coeftest(m1b1_2, vcovHC(m1b1_2, "HC2"))[18,],
coeftest(m2b1_2, vcovHC(m2b1_2, "HC2"))[18,],
coeftest(m3b1_2, vcovHC(m3b1_2, "HC2"))[18,],
coeftest(m4b1_2, vcovHC(m4b1_2, "HC2"))[18,],
coeftest(nm1b1_1, vcovHC(nm1b1_1, "HC2"))[8,],
coeftest(nm2b1_1, vcovHC(nm2b1_1, "HC2"))[8,],
coeftest(nm3b1_1, vcovHC(nm3b1_1, "HC2"))[8,],
coeftest(nm4b1_1, vcovHC(nm4b1_1, "HC2"))[8,],
coeftest(nm1b1_2, vcovHC(nm1b1_2, "HC2"))[18,],
coeftest(nm2b1_2, vcovHC(nm2b1_2, "HC2"))[18,],
coeftest(nm3b1_2, vcovHC(nm3b1_2, "HC2"))[18,],
coeftest(nm4b1_2, vcovHC(nm4b1_2, "HC2"))[18,],
coeftest(fm1b1_1, vcovHC(fm1b1_1, "HC2"))[8,],
coeftest(fm2b1_1, vcovHC(fm2b1_1, "HC2"))[8,],
coeftest(fm3b1_1, vcovHC(fm3b1_1, "HC2"))[8,],
coeftest(fm4b1_1, vcovHC(fm4b1_1, "HC2"))[8,],
coeftest(fm1b1_2, vcovHC(fm1b1_2, "HC2"))[18,],
coeftest(fm2b1_2, vcovHC(fm2b1_2, "HC2"))[18,],
coeftest(fm3b1_2, vcovHC(fm3b1_2, "HC2"))[18,],
coeftest(fm4b1_2, vcovHC(fm4b1_2, "HC2"))[18,])
# H3A
h3acf <- rbind(coeftest(m1b1_1, vcovHC(m1b1_1, "HC2"))[9,],
coeftest(m2b1_1, vcovHC(m2b1_1, "HC2"))[9,],
coeftest(m3b1_1, vcovHC(m3b1_1, "HC2"))[9,],
coeftest(m4b1_1, vcovHC(m4b1_1, "HC2"))[9,],
coeftest(m1b1_2, vcovHC(m1b1_2, "HC2"))[19,],
coeftest(m2b1_2, vcovHC(m2b1_2, "HC2"))[19,],
coeftest(m3b1_2, vcovHC(m3b1_2, "HC2"))[19,],
coeftest(m4b1_2, vcovHC(m4b1_2, "HC2"))[19,],
coeftest(nm1b1_1, vcovHC(nm1b1_1, "HC2"))[9,],
coeftest(nm2b1_1, vcovHC(nm2b1_1, "HC2"))[9,],
coeftest(nm3b1_1, vcovHC(nm3b1_1, "HC2"))[9,],
coeftest(nm4b1_1, vcovHC(nm4b1_1, "HC2"))[9,],
coeftest(nm1b1_2, vcovHC(nm1b1_2, "HC2"))[19,],
coeftest(nm2b1_2, vcovHC(nm2b1_2, "HC2"))[19,],
coeftest(nm3b1_2, vcovHC(nm3b1_2, "HC2"))[19,],
coeftest(nm4b1_2, vcovHC(nm4b1_2, "HC2"))[19,],
coeftest(fm1b1_1, vcovHC(fm1b1_1, "HC2"))[9,],
coeftest(fm2b1_1, vcovHC(fm2b1_1, "HC2"))[9,],
coeftest(fm3b1_1, vcovHC(fm3b1_1, "HC2"))[9,],
coeftest(fm4b1_1, vcovHC(fm4b1_1, "HC2"))[9,],
coeftest(fm1b1_2, vcovHC(fm1b1_2, "HC2"))[19,],
coeftest(fm2b1_2, vcovHC(fm2b1_2, "HC2"))[19,],
coeftest(fm3b1_2, vcovHC(fm3b1_2, "HC2"))[19,],
coeftest(fm4b1_2, vcovHC(fm4b1_2, "HC2"))[19,])
# H3B
h3bcf <- rbind(coeftest(m1b2_1, vcovHC(m1b2_1, "HC2"))[10,],
coeftest(m2b2_1, vcovHC(m2b2_1, "HC2"))[10,],
coeftest(m3b2_1, vcovHC(m3b2_1, "HC2"))[10,],
coeftest(m4b2_1, vcovHC(m4b2_1, "HC2"))[10,],
coeftest(m1b2_2, vcovHC(m1b2_2, "HC2"))[20,],
coeftest(m2b2_2, vcovHC(m2b2_2, "HC2"))[20,],
coeftest(m3b2_2, vcovHC(m3b2_2, "HC2"))[20,],
coeftest(m4b2_2, vcovHC(m4b2_2, "HC2"))[20,],
coeftest(nm1b2_1, vcovHC(nm1b2_1, "HC2"))[10,],
coeftest(nm2b2_1, vcovHC(nm2b2_1, "HC2"))[10,],
coeftest(nm3b2_1, vcovHC(nm3b2_1, "HC2"))[10,],
coeftest(nm4b2_1, vcovHC(nm4b2_1, "HC2"))[10,],
coeftest(nm1b2_2, vcovHC(nm1b2_2, "HC2"))[20,],
coeftest(nm2b2_2, vcovHC(nm2b2_2, "HC2"))[20,],
coeftest(nm3b2_2, vcovHC(nm3b2_2, "HC2"))[20,],
coeftest(nm4b2_2, vcovHC(nm4b2_2, "HC2"))[20,],
coeftest(fm1b2_1, vcovHC(fm1b2_1, "HC2"))[10,],
coeftest(fm2b2_1, vcovHC(fm2b2_1, "HC2"))[10,],
coeftest(fm3b2_1, vcovHC(fm3b2_1, "HC2"))[10,],
coeftest(fm4b2_1, vcovHC(fm4b2_1, "HC2"))[10,],
coeftest(fm1b2_2, vcovHC(fm1b2_2, "HC2"))[20,],
coeftest(fm2b2_2, vcovHC(fm2b2_2, "HC2"))[20,],
coeftest(fm3b2_2, vcovHC(fm3b2_2, "HC2"))[20,],
coeftest(fm4b2_2, vcovHC(fm4b2_2, "HC2"))[20,])
htest <- as.data.frame(rbind(h1cf,h2cf,h3acf,h3bcf))
names(htest) <- c("est","se","tval","pval")
htest$meth <- rep(c("main_base","main_ext",
"mis_base","mis_ext",
"dropfail_base","dropfail_ext"), each=4)
htest$hyp <- rep(c("H1:1.経済成長\nv.s.統制群",
"H2:2.経済成長&貧困削減\nv.s.1.経済成長",
"H3:3.経済成長&学者賛成\nv.s.1.経済成長",
"H3:4.成長&貧困&学者\nv.s.2.経済成長&貧困削減"),
each=24)
htest$hyp <- factor(htest$hyp, levels=rev(unique(htest$hyp)))
htest$ms <- c("自己申告","政党支持","外交安全保障","権利機会平等")
htest$ms <- factor(htest$ms, levels=unique(htest$ms))
htest$l90CI <- htest$est - htest$se*qnorm(0.95)
htest$u90CI <- htest$est + htest$se*qnorm(0.95)
htest$l95CI <- htest$est - htest$se*qnorm(0.975)
htest$u95CI <- htest$est + htest$se*qnorm(0.975)
htest$ptest <- ifelse(htest$pval<0.05,"p<.05",
ifelse(htest$pval<0.10,"p<.10","n.s.(p>=.10)"))
htest$ptest <- factor(htest$ptest, levels=c("p<.05","p<.10","n.s.(p>=.10)"))exporthtestplot <- function(meth, captiontxt) {
p <- ggplot(htest[htest$meth==meth,], aes_string(x="hyp")) +
geom_hline(aes(yintercept=0), linetype=2) +
geom_errorbar(aes_string(ymin="l95CI",ymax="u95CI",
color="ptest"),width=0.1) +
geom_errorbar(aes_string(ymin="l90CI",ymax="u90CI",
color="ptest"),width=0,size=0.8) +
geom_point(aes_string(y="est",color="ptest",shape="ptest"),size=2) +
scale_y_continuous(breaks=c(-0.3,0,0.3)) +
scale_color_manual(name="", values=c("red2","darkorange2","gray50"),
drop=FALSE) +
scale_shape_discrete(name="", drop=FALSE) +
facet_grid(.~ms) +
xlab(NULL) + ylab("イデオロギー交差項の係数+95%信頼区間\n(太線は90%信頼区間)") +
labs(subtitle="イデオロギー指標",
caption=captiontxt) +
coord_flip() + theme_bw() +
theme(plot.margin = unit(c(0.5,0.5,0.5,0.1), "cm"),
panel.grid = element_line(color=NA),
plot.subtitle = element_text(hjust=0.5),
axis.text.y = element_text(color="black"),
legend.position = "bottom")
p
}ggsave("../out/v6_htestres_2.png", p, width=8, height=5)
# ggsave("../out/v6_htestres_2.pdf", p, width=8, height=5, family = "Japan1")table_coef(list(m1b0_1,m2b0_1,m3b0_1,m4b0_1),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
custom.variable.names = c(vn[1:5],rep(vn_idex,4)),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="実験情報刺激が金融緩和選好に与える効果に対するイデオロギーの条件付け(統制変数無;金融緩和選好とイデオロギー変数の「わからない」回答には0を代入)",
label="idetab_1", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).",
format = "tex", file.name = "../out/v6_idetab_1")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT as.factor(g_easing_N)1 1.経済成長
## KEPT as.factor(g_easing_N)2 2.経済成長&貧困削減
## KEPT as.factor(g_easing_N)3 3.経済成長&学者賛成
## KEPT as.factor(g_easing_N)4 4.経済成長&貧困&学者
## KEPT ide_self イデオロギー
## KEPT as.factor(g_easing_N)1:ide_self イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_self イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_self イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_self イデオロギー×4.成長&貧困&学者
## KEPT ide_psup イデオロギー
## KEPT as.factor(g_easing_N)1:ide_psup イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_psup イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_psup イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_psup イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_1 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_1 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_1 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_1 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_1 イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_2 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_2 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_2 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_2 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_2 イデオロギー×4.成長&貧困&学者
## The table was written to the file '../out/v6_idetab_1.tex'.
##
## =====================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## ---------------------------------------------------------------------
## (定数項) 0.775 *** 0.648 *** 0.755 *** 0.772 ***
## (0.087) (0.088) (0.083) (0.090)
## 1.経済成長 0.108 0.214 0.143 0.128
## (0.131) (0.132) (0.123) (0.133)
## 2.経済成長&貧困削減 0.293 * 0.355 ** 0.310 * 0.306 *
## (0.127) (0.133) (0.122) (0.129)
## 3.経済成長&学者賛成 0.140 0.220 + 0.155 0.158
## (0.123) (0.131) (0.117) (0.125)
## 4.経済成長&貧困&学者 0.361 ** 0.493 *** 0.387 ** 0.368 **
## (0.121) (0.124) (0.117) (0.123)
## イデオロギー 0.169 + 0.374 ** 0.362 *** 0.083
## (0.087) (0.119) (0.081) (0.073)
## イデオロギー×1.成長 0.048 -0.119 0.103 -0.190
## (0.126) (0.180) (0.130) (0.124)
## イデオロギー×2.成長&貧困 0.067 -0.045 -0.011 -0.208 *
## (0.121) (0.172) (0.118) (0.104)
## イデオロギー×3.成長&学者 0.029 -0.116 -0.005 -0.183 +
## (0.116) (0.175) (0.109) (0.105)
## イデオロギー×4.成長&貧困&学者 -0.261 * -0.417 * -0.169 -0.029
## (0.124) (0.167) (0.113) (0.106)
## ---------------------------------------------------------------------
## R^2 0.041 0.038 0.107 0.019
## Adj. R^2 0.031 0.029 0.099 0.010
## Num. obs. 927 927 927 927
## RMSE 1.213 1.215 1.170 1.227
## =====================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).
tmp <- readLines("../out/v6_idetab_1.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_idetab_1.tex", useBytes = TRUE)table_coef(list(m1b0_2,m2b0_2,m3b0_2,m4b0_2),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
custom.variable.names = vnx,
order.variable = c(1:6,17:35,7:16),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="実験情報刺激が金融緩和選好に与える効果に対するイデオロギーの条件付け(統制変数有;金融緩和選好とイデオロギー変数の「わからない」回答には0を代入)",
label="idetab_2", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).",
format = "tex", file.name = "../out/v6_idetab_2")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT as.factor(g_easing_N)1 1.経済成長
## KEPT as.factor(g_easing_N)2 2.経済成長&貧困削減
## KEPT as.factor(g_easing_N)3 3.経済成長&学者賛成
## KEPT as.factor(g_easing_N)4 4.経済成長&貧困&学者
## KEPT ide_self イデオロギー
## KEPT knall 政治知識
## KEPT fem 性別(女性)
## KEPT age 年齢
## KEPT lvlen 居住年数
## KEPT ownh 持ち家
## KEPT as.factor(edu3)1 教育:短大/高専/専門学校
## KEPT as.factor(edu3)2 教育:大卒以上
## KEPT wk 就労
## KEPT mar 婚姻
## KEPT cld 子ども
## KEPT as.factor(g_easing_N)1:ide_self イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_self イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_self イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_self イデオロギー×4.成長&貧困&学者
## KEPT ide_psup イデオロギー
## KEPT as.factor(g_easing_N)1:ide_psup イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_psup イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_psup イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_psup イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_1 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_1 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_1 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_1 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_1 イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_2 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_2 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_2 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_2 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_2 イデオロギー×4.成長&貧困&学者
## The table was written to the file '../out/v6_idetab_2.tex'.
##
## =====================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## ---------------------------------------------------------------------
## (定数項) 1.102 *** 0.959 *** 0.950 *** 1.030 ***
## (0.224) (0.224) (0.212) (0.223)
## 1.経済成長 0.128 0.238 + 0.156 0.154
## (0.129) (0.130) (0.121) (0.130)
## 2.経済成長&貧困削減 0.279 * 0.350 ** 0.304 * 0.294 *
## (0.126) (0.133) (0.121) (0.127)
## 3.経済成長&学者賛成 0.137 0.228 + 0.159 0.159
## (0.121) (0.130) (0.116) (0.123)
## 4.経済成長&貧困&学者 0.382 ** 0.519 *** 0.405 *** 0.395 **
## (0.119) (0.122) (0.116) (0.121)
## イデオロギー 0.159 + 0.368 ** 0.351 *** 0.082
## (0.085) (0.116) (0.077) (0.069)
## イデオロギー×1.成長 0.033 -0.157 0.088 -0.220 +
## (0.123) (0.177) (0.127) (0.121)
## イデオロギー×2.成長&貧困 0.049 -0.071 -0.006 -0.211 *
## (0.120) (0.169) (0.115) (0.101)
## イデオロギー×3.成長&学者 0.025 -0.155 -0.034 -0.196 *
## (0.112) (0.170) (0.105) (0.098)
## イデオロギー×4.成長&貧困&学者 -0.272 * -0.441 ** -0.211 + -0.037
## (0.124) (0.166) (0.111) (0.104)
## 政治知識 0.262 + 0.251 0.220 0.283 +
## (0.155) (0.156) (0.149) (0.158)
## 性別(女性) -0.324 *** -0.313 *** -0.210 * -0.393 ***
## (0.088) (0.088) (0.087) (0.090)
## 年齢 -0.007 + -0.006 -0.004 -0.005
## (0.004) (0.004) (0.004) (0.004)
## 居住年数 -0.071 * -0.066 * -0.063 * -0.064 *
## (0.032) (0.031) (0.030) (0.032)
## 持ち家 0.003 -0.007 -0.017 0.012
## (0.090) (0.089) (0.087) (0.091)
## 教育:短大/高専/専門学校 0.175 0.205 0.179 0.180
## (0.140) (0.140) (0.137) (0.139)
## 教育:大卒以上 0.088 0.114 0.149 0.093
## (0.110) (0.112) (0.107) (0.112)
## 就労 0.014 -0.013 -0.019 -0.012
## (0.091) (0.092) (0.088) (0.091)
## 婚姻 -0.132 -0.192 -0.198 + -0.119
## (0.120) (0.123) (0.118) (0.122)
## 子ども 0.211 + 0.243 * 0.268 * 0.236 +
## (0.120) (0.122) (0.118) (0.123)
## ---------------------------------------------------------------------
## R^2 0.077 0.074 0.132 0.062
## Adj. R^2 0.058 0.055 0.114 0.043
## Num. obs. 927 927 927 927
## RMSE 1.197 1.199 1.160 1.206
## =====================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).
tmp <- readLines("../out/v6_idetab_2.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_idetab_2.tex", useBytes = TRUE)table_coef(list(m1b0_2,m2b0_2,m3b0_2,m4b0_2),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
drop.intercept = TRUE,
drop.variable.names = c("knall","fem","age","lvlen","ownh",
"as.factor(edu3)1","as.factor(edu3)2",
"wk","mar","cld"),
custom.variable.names = c(vn[2:5],rep(vn_idex,4)),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="実験情報刺激が金融緩和選好に与える効果に対するイデオロギーの条件付け",
label="idetab_2_short", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).定数項・統制変数の係数はオンライン付録Hを参照.",
format = "tex", file.name = "../out/v6_idetab_2_short")## Variable Manipulations:
## Omitted Original Final
## OMITTED (Intercept)
## KEPT as.factor(g_easing_N)1 1.経済成長
## KEPT as.factor(g_easing_N)2 2.経済成長&貧困削減
## KEPT as.factor(g_easing_N)3 3.経済成長&学者賛成
## KEPT as.factor(g_easing_N)4 4.経済成長&貧困&学者
## KEPT ide_self イデオロギー
## OMITTED knall
## OMITTED fem
## OMITTED age
## OMITTED lvlen
## OMITTED ownh
## OMITTED as.factor(edu3)1
## OMITTED as.factor(edu3)2
## OMITTED wk
## OMITTED mar
## OMITTED cld
## KEPT as.factor(g_easing_N)1:ide_self イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_self イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_self イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_self イデオロギー×4.成長&貧困&学者
## KEPT ide_psup イデオロギー
## KEPT as.factor(g_easing_N)1:ide_psup イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_psup イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_psup イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_psup イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_1 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_1 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_1 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_1 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_1 イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_2 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_2 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_2 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_2 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_2 イデオロギー×4.成長&貧困&学者
## The table was written to the file '../out/v6_idetab_2_short.tex'.
##
## ===================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## -------------------------------------------------------------------
## 1.経済成長 0.128 0.238 + 0.156 0.154
## (0.129) (0.130) (0.121) (0.130)
## 2.経済成長&貧困削減 0.279 * 0.350 ** 0.304 * 0.294 *
## (0.126) (0.133) (0.121) (0.127)
## 3.経済成長&学者賛成 0.137 0.228 + 0.159 0.159
## (0.121) (0.130) (0.116) (0.123)
## 4.経済成長&貧困&学者 0.382 ** 0.519 *** 0.405 *** 0.395 **
## (0.119) (0.122) (0.116) (0.121)
## イデオロギー 0.159 + 0.368 ** 0.351 *** 0.082
## (0.085) (0.116) (0.077) (0.069)
## イデオロギー×1.成長 0.033 -0.157 0.088 -0.220 +
## (0.123) (0.177) (0.127) (0.121)
## イデオロギー×2.成長&貧困 0.049 -0.071 -0.006 -0.211 *
## (0.120) (0.169) (0.115) (0.101)
## イデオロギー×3.成長&学者 0.025 -0.155 -0.034 -0.196 *
## (0.112) (0.170) (0.105) (0.098)
## イデオロギー×4.成長&貧困&学者 -0.272 * -0.441 ** -0.211 + -0.037
## (0.124) (0.166) (0.111) (0.104)
## -------------------------------------------------------------------
## R^2 0.077 0.074 0.132 0.062
## Adj. R^2 0.058 0.055 0.114 0.043
## Num. obs. 927 927 927 927
## RMSE 1.197 1.199 1.160 1.206
## ===================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).定数項・統制変数の係数はオンライン付録Hを参照.
tmp <- readLines("../out/v6_idetab_2_short.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_idetab_2_short.tex", useBytes = TRUE)
# table_coef(list(m1b0_2,m2b0_2,m3b0_2,m4b0_2),
# vcov.est="robust", robust.type="HC2",
# single.row=TRUE,
# drop.intercept = TRUE,
# drop.variable.names = c("knall","fem","age","lvlen","ownh",
# "as.factor(edu3)1","as.factor(edu3)2",
# "wk","mar","cld"),
# custom.variable.names = c(vn[2:5],rep(vn_idex,4)),
# m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
# caption="実験情報刺激が金融緩和選好に与える効果に対するイデオロギーの条件付け",
# label="idetab_2_short", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
# custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).定数項・統制変数の係数はオンライン付録Hを参照.",
# show.table = FALSE,
# format = "doc", file.name = "../out/v6_idetab_2_short")table_coef(list(nm1b0_1,nm2b0_1,nm3b0_1,nm4b0_1),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
custom.variable.names = c(vn[1:5],rep(vn_idex,4)),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="実験情報刺激が金融緩和選好に与える効果に対するイデオロギーの条件付け(統制変数無;金融緩和選好とイデオロギー変数の「わからない」回答は分析から除外)",
label="idetab_n1", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).",
format = "tex", file.name = "../out/v6_idetab_n1")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT as.factor(g_easing_N)1 1.経済成長
## KEPT as.factor(g_easing_N)2 2.経済成長&貧困削減
## KEPT as.factor(g_easing_N)3 3.経済成長&学者賛成
## KEPT as.factor(g_easing_N)4 4.経済成長&貧困&学者
## KEPT ide_self_mis イデオロギー
## KEPT as.factor(g_easing_N)1:ide_self_mis イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_self_mis イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_self_mis イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_self_mis イデオロギー×4.成長&貧困&学者
## KEPT ide_psup_mis イデオロギー
## KEPT as.factor(g_easing_N)1:ide_psup_mis イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_psup_mis イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_psup_mis イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_psup_mis イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_mis_1 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_mis_1 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_mis_1 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_mis_1 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_mis_1 イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_mis_2 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_mis_2 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_mis_2 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_mis_2 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_mis_2 イデオロギー×4.成長&貧困&学者
## The table was written to the file '../out/v6_idetab_n1.tex'.
##
## =====================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## ---------------------------------------------------------------------
## (定数項) 1.010 *** 0.838 *** 1.026 *** 1.082 ***
## (0.107) (0.116) (0.114) (0.140)
## 1.経済成長 0.042 0.135 -0.095 -0.155
## (0.156) (0.157) (0.174) (0.202)
## 2.経済成長&貧困削減 0.119 0.266 + 0.198 0.188
## (0.147) (0.159) (0.153) (0.177)
## 3.経済成長&学者賛成 0.008 0.069 -0.140 -0.145
## (0.144) (0.158) (0.164) (0.185)
## 4.経済成長&貧困&学者 0.217 0.399 ** 0.268 + 0.230
## (0.141) (0.148) (0.155) (0.176)
## イデオロギー 0.176 + 0.395 ** 0.442 *** 0.008
## (0.096) (0.150) (0.095) (0.114)
## イデオロギー×1.成長 0.064 -0.103 0.181 0.009
## (0.132) (0.209) (0.177) (0.184)
## イデオロギー×2.成長&貧困 0.081 -0.064 -0.006 -0.102
## (0.128) (0.198) (0.133) (0.145)
## イデオロギー×3.成長&学者 0.021 -0.137 -0.137 -0.073
## (0.123) (0.203) (0.138) (0.151)
## イデオロギー×4.成長&貧困&学者 -0.233 + -0.424 * -0.288 * 0.006
## (0.133) (0.193) (0.139) (0.142)
## ---------------------------------------------------------------------
## R^2 0.044 0.036 0.146 0.019
## Adj. R^2 0.032 0.025 0.131 0.002
## Num. obs. 724 806 532 532
## RMSE 1.218 1.239 1.167 1.251
## =====================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).
tmp <- readLines("../out/v6_idetab_n1.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_idetab_n1.tex", useBytes = TRUE)table_coef(list(nm1b0_2,nm2b0_2,nm3b0_2,nm4b0_2),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
custom.variable.names = vnx,
order.variable = c(1:6,17:35,7:16),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="実験情報刺激が金融緩和選好に与える効果に対するイデオロギーの条件付け(統制変数有;金融緩和選好とイデオロギー変数の「わからない」回答は分析から除外)",
label="idetab_n2", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).",
format = "tex", file.name = "../out/v6_idetab_n2")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT as.factor(g_easing_N)1 1.経済成長
## KEPT as.factor(g_easing_N)2 2.経済成長&貧困削減
## KEPT as.factor(g_easing_N)3 3.経済成長&学者賛成
## KEPT as.factor(g_easing_N)4 4.経済成長&貧困&学者
## KEPT ide_self_mis イデオロギー
## KEPT knall 政治知識
## KEPT fem 性別(女性)
## KEPT age 年齢
## KEPT lvlen 居住年数
## KEPT ownh 持ち家
## KEPT as.factor(edu3)1 教育:短大/高専/専門学校
## KEPT as.factor(edu3)2 教育:大卒以上
## KEPT wk 就労
## KEPT mar 婚姻
## KEPT cld 子ども
## KEPT as.factor(g_easing_N)1:ide_self_mis イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_self_mis イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_self_mis イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_self_mis イデオロギー×4.成長&貧困&学者
## KEPT ide_psup_mis イデオロギー
## KEPT as.factor(g_easing_N)1:ide_psup_mis イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_psup_mis イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_psup_mis イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_psup_mis イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_mis_1 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_mis_1 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_mis_1 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_mis_1 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_mis_1 イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_mis_2 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_mis_2 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_mis_2 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_mis_2 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_mis_2 イデオロギー×4.成長&貧困&学者
## The table was written to the file '../out/v6_idetab_n2.tex'.
##
## =====================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## ---------------------------------------------------------------------
## (定数項) 1.323 *** 1.184 *** 1.118 *** 1.250 ***
## (0.261) (0.260) (0.298) (0.322)
## 1.経済成長 0.067 0.190 -0.053 -0.059
## (0.155) (0.155) (0.171) (0.198)
## 2.経済成長&貧困削減 0.127 0.292 + 0.224 0.233
## (0.146) (0.158) (0.152) (0.175)
## 3.経済成長&学者賛成 0.018 0.100 -0.123 -0.100
## (0.143) (0.156) (0.163) (0.183)
## 4.経済成長&貧困&学者 0.252 + 0.448 ** 0.324 * 0.314 +
## (0.141) (0.146) (0.155) (0.176)
## イデオロギー 0.183 + 0.413 ** 0.433 *** 0.027
## (0.095) (0.145) (0.092) (0.108)
## イデオロギー×1.成長 0.033 -0.178 0.176 -0.069
## (0.129) (0.206) (0.171) (0.179)
## イデオロギー×2.成長&貧困 0.052 -0.117 -0.003 -0.133
## (0.128) (0.195) (0.132) (0.138)
## イデオロギー×3.成長&学者 0.004 -0.185 -0.156 -0.128
## (0.120) (0.198) (0.137) (0.143)
## イデオロギー×4.成長&貧困&学者 -0.254 + -0.464 * -0.348 * -0.023
## (0.135) (0.192) (0.143) (0.142)
## 政治知識 0.021 0.089 -0.057 0.042
## (0.180) (0.174) (0.201) (0.218)
## 性別(女性) -0.285 ** -0.308 ** -0.331 ** -0.528 ***
## (0.101) (0.097) (0.119) (0.119)
## 年齢 -0.006 -0.006 -0.003 -0.002
## (0.005) (0.005) (0.005) (0.006)
## 居住年数 -0.073 + -0.062 + -0.073 + -0.084 +
## (0.038) (0.036) (0.042) (0.047)
## 持ち家 0.024 0.002 0.045 0.093
## (0.105) (0.099) (0.116) (0.129)
## 教育:短大/高専/専門学校 0.161 0.172 0.261 0.155
## (0.165) (0.154) (0.193) (0.202)
## 教育:大卒以上 0.133 0.114 0.276 + 0.145
## (0.133) (0.128) (0.160) (0.171)
## 就労 0.049 -0.026 0.033 0.013
## (0.106) (0.102) (0.130) (0.134)
## 婚姻 -0.209 -0.199 -0.190 -0.093
## (0.141) (0.133) (0.149) (0.161)
## 子ども 0.313 * 0.235 + 0.374 * 0.264
## (0.138) (0.133) (0.149) (0.163)
## ---------------------------------------------------------------------
## R^2 0.076 0.064 0.186 0.077
## Adj. R^2 0.051 0.041 0.155 0.042
## Num. obs. 724 806 532 532
## RMSE 1.206 1.229 1.151 1.225
## =====================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).
tmp <- readLines("../out/v6_idetab_n2.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_idetab_n2.tex", useBytes = TRUE)table_coef(list(fm1b0_1,fm2b0_1,fm3b0_1,fm4b0_1),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
custom.variable.names = c(vn[1:5],rep(vn_idex,4)),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="実験情報刺激が金融緩和選好に与える効果に対するイデオロギーの条件付け(統制変数無;マニピュレーションチェックに違反した回答者を分析から除外)",
label="idetab_f1", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).",
format = "tex", file.name = "../out/v6_idetab_f1")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT as.factor(g_easing_N)1 1.経済成長
## KEPT as.factor(g_easing_N)2 2.経済成長&貧困削減
## KEPT as.factor(g_easing_N)3 3.経済成長&学者賛成
## KEPT as.factor(g_easing_N)4 4.経済成長&貧困&学者
## KEPT ide_self イデオロギー
## KEPT as.factor(g_easing_N)1:ide_self イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_self イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_self イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_self イデオロギー×4.成長&貧困&学者
## KEPT ide_psup イデオロギー
## KEPT as.factor(g_easing_N)1:ide_psup イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_psup イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_psup イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_psup イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_1 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_1 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_1 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_1 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_1 イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_2 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_2 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_2 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_2 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_2 イデオロギー×4.成長&貧困&学者
## The table was written to the file '../out/v6_idetab_f1.tex'.
##
## =====================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## ---------------------------------------------------------------------
## (定数項) 0.706 *** 0.558 *** 0.710 *** 0.704 ***
## (0.097) (0.096) (0.094) (0.098)
## 1.経済成長 0.105 0.235 + 0.122 0.116
## (0.140) (0.139) (0.133) (0.141)
## 2.経済成長&貧困削減 0.335 * 0.412 ** 0.341 * 0.350 *
## (0.135) (0.140) (0.133) (0.138)
## 3.経済成長&学者賛成 0.227 + 0.325 * 0.216 + 0.235 +
## (0.132) (0.140) (0.127) (0.133)
## 4.経済成長&貧困&学者 0.451 *** 0.601 *** 0.461 *** 0.462 ***
## (0.130) (0.131) (0.128) (0.131)
## イデオロギー 0.143 0.443 *** 0.307 *** 0.071
## (0.096) (0.131) (0.091) (0.082)
## イデオロギー×1.成長 0.091 -0.199 0.157 -0.219
## (0.135) (0.192) (0.140) (0.136)
## イデオロギー×2.成長&貧困 0.131 -0.092 0.012 -0.168
## (0.130) (0.183) (0.132) (0.114)
## イデオロギー×3.成長&学者 0.045 -0.210 0.031 -0.181
## (0.124) (0.186) (0.119) (0.114)
## イデオロギー×4.成長&貧困&学者 -0.237 + -0.451 * -0.111 0.003
## (0.132) (0.177) (0.122) (0.114)
## ---------------------------------------------------------------------
## R^2 0.049 0.046 0.101 0.025
## Adj. R^2 0.039 0.035 0.091 0.015
## Num. obs. 833 833 833 833
## RMSE 1.205 1.207 1.171 1.220
## =====================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).
tmp <- readLines("../out/v6_idetab_f1.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_idetab_f1.tex", useBytes = TRUE)table_coef(list(fm1b0_2,fm2b0_2,fm3b0_2,fm4b0_2),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
custom.variable.names = vnx,
order.variable = c(1:6,17:35,7:16),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="実験情報刺激が金融緩和選好に与える効果に対するイデオロギーの条件付け(統制変数有;マニピュレーションチェックに違反した回答者を分析から除外)",
label="idetab_f2", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).",
format = "tex", file.name = "../out/v6_idetab_f2")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT as.factor(g_easing_N)1 1.経済成長
## KEPT as.factor(g_easing_N)2 2.経済成長&貧困削減
## KEPT as.factor(g_easing_N)3 3.経済成長&学者賛成
## KEPT as.factor(g_easing_N)4 4.経済成長&貧困&学者
## KEPT ide_self イデオロギー
## KEPT knall 政治知識
## KEPT fem 性別(女性)
## KEPT age 年齢
## KEPT lvlen 居住年数
## KEPT ownh 持ち家
## KEPT as.factor(edu3)1 教育:短大/高専/専門学校
## KEPT as.factor(edu3)2 教育:大卒以上
## KEPT wk 就労
## KEPT mar 婚姻
## KEPT cld 子ども
## KEPT as.factor(g_easing_N)1:ide_self イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_self イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_self イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_self イデオロギー×4.成長&貧困&学者
## KEPT ide_psup イデオロギー
## KEPT as.factor(g_easing_N)1:ide_psup イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_psup イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_psup イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_psup イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_1 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_1 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_1 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_1 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_1 イデオロギー×4.成長&貧困&学者
## KEPT ide_iss_2 イデオロギー
## KEPT as.factor(g_easing_N)1:ide_iss_2 イデオロギー×1.成長
## KEPT as.factor(g_easing_N)2:ide_iss_2 イデオロギー×2.成長&貧困
## KEPT as.factor(g_easing_N)3:ide_iss_2 イデオロギー×3.成長&学者
## KEPT as.factor(g_easing_N)4:ide_iss_2 イデオロギー×4.成長&貧困&学者
## The table was written to the file '../out/v6_idetab_f2.tex'.
##
## =====================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## ---------------------------------------------------------------------
## (定数項) 1.093 *** 0.934 *** 0.930 *** 1.014 ***
## (0.238) (0.237) (0.227) (0.235)
## 1.経済成長 0.118 0.253 + 0.134 0.134
## (0.137) (0.136) (0.131) (0.138)
## 2.経済成長&貧困削減 0.311 * 0.399 ** 0.333 * 0.325 *
## (0.133) (0.138) (0.131) (0.135)
## 3.経済成長&学者賛成 0.219 + 0.331 * 0.221 + 0.228 +
## (0.129) (0.137) (0.125) (0.130)
## 4.経済成長&貧困&学者 0.469 *** 0.623 *** 0.476 *** 0.484 ***
## (0.128) (0.129) (0.126) (0.129)
## イデオロギー 0.140 0.440 *** 0.307 *** 0.063
## (0.095) (0.126) (0.087) (0.077)
## イデオロギー×1.成長 0.076 -0.237 0.134 -0.231 +
## (0.133) (0.187) (0.138) (0.132)
## イデオロギー×2.成長&貧困 0.111 -0.114 0.002 -0.152
## (0.128) (0.178) (0.129) (0.110)
## イデオロギー×3.成長&学者 0.030 -0.245 -0.012 -0.181 +
## (0.120) (0.179) (0.114) (0.106)
## イデオロギー×4.成長&貧困&学者 -0.242 + -0.468 ** -0.160 0.009
## (0.133) (0.174) (0.122) (0.110)
## 政治知識 0.264 0.250 0.236 0.271
## (0.162) (0.163) (0.156) (0.165)
## 性別(女性) -0.303 ** -0.288 ** -0.196 * -0.363 ***
## (0.093) (0.094) (0.093) (0.096)
## 年齢 -0.007 -0.006 -0.004 -0.005
## (0.004) (0.004) (0.004) (0.004)
## 居住年数 -0.083 * -0.077 * -0.076 * -0.075 *
## (0.033) (0.033) (0.031) (0.033)
## 持ち家 0.019 0.015 0.018 0.040
## (0.094) (0.094) (0.092) (0.096)
## 教育:短大/高専/専門学校 0.139 0.174 0.147 0.155
## (0.150) (0.149) (0.146) (0.149)
## 教育:大卒以上 0.058 0.087 0.128 0.084
## (0.118) (0.121) (0.115) (0.120)
## 就労 -0.013 -0.044 -0.045 -0.043
## (0.094) (0.095) (0.092) (0.095)
## 婚姻 -0.193 -0.262 * -0.239 + -0.181
## (0.125) (0.129) (0.124) (0.128)
## 子ども 0.289 * 0.317 * 0.325 ** 0.317 *
## (0.124) (0.127) (0.123) (0.128)
## ---------------------------------------------------------------------
## R^2 0.087 0.084 0.129 0.069
## Adj. R^2 0.066 0.062 0.109 0.047
## Num. obs. 833 833 833 833
## RMSE 1.187 1.190 1.160 1.199
## =====================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).
tmp <- readLines("../out/v6_idetab_f2.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_idetab_f2.tex", useBytes = TRUE)# Main Models (With Different Base Category)
m1h2xb0_1 <- lm(easing_opi ~ g_h2x*ide_self, data=dtmp)
m1h2xb0_2 <- lm(update(easing_opi ~ g_h2x*ide_self,ctl), data=dtmp)
m1h2xb1_1 <- lm(easing_opi ~ I(1-g_h2x)*ide_self, data=dtmp)
m1h2xb1_2 <- lm(update(easing_opi ~ I(1-g_h2x)*ide_self,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
m1h2xmg <- data.frame(
tr = c(0,1), ide="自己申告", mod=c(1,1,2,2),
rbind(coeftest(m1h2xb0_1, vcovHC(m1h2xb0_1, "HC2"))[3,],
coeftest(m1h2xb1_1, vcovHC(m1h2xb1_1, "HC2"))[3,],
coeftest(m1h2xb0_2, vcovHC(m1h2xb0_2, "HC2"))[3,],
coeftest(m1h2xb1_2, vcovHC(m1h2xb1_2, "HC2"))[3,])
)
names(m1h2xmg)[4:7] <- c("est","se","tval","pval")
# Models with Missing Values as NA
nm1h2xb0_1 <- lm(easing_opi_mis ~ g_h2x*ide_self_mis, data=dtmp)
nm1h2xb0_2 <- lm(update(easing_opi_mis ~ g_h2x*ide_self_mis,ctl), data=dtmp)
nm1h2xb1_1 <- lm(easing_opi_mis ~ I(1-g_h2x)*ide_self_mis, data=dtmp)
nm1h2xb1_2 <- lm(update(easing_opi_mis ~ I(1-g_h2x)*ide_self_mis,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
nm1h2xmg <- data.frame(
tr = c(0,1), ide="自己申告", mod=c(1,1,2,2),
rbind(coeftest(nm1h2xb0_1, vcovHC(nm1h2xb0_1, "HC2"))[3,],
coeftest(nm1h2xb1_1, vcovHC(nm1h2xb1_1, "HC2"))[3,],
coeftest(nm1h2xb0_2, vcovHC(nm1h2xb0_2, "HC2"))[3,],
coeftest(nm1h2xb1_2, vcovHC(nm1h2xb1_2, "HC2"))[3,])
)
names(nm1h2xmg)[4:7] <- c("est","se","tval","pval")
# Models without Respondents Failing Manipulation Check
fm1h2xb0_1 <- lm(easing_opi ~ g_h2x*ide_self, data=dtmp[which(dtmp$check_fail==0),])
fm1h2xb0_2 <- lm(update(easing_opi ~ g_h2x*ide_self,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm1h2xb1_1 <- lm(easing_opi ~ I(1-g_h2x)*ide_self, data=dtmp[which(dtmp$check_fail==0),])
fm1h2xb1_2 <- lm(update(easing_opi ~ I(1-g_h2x)*ide_self,ctl), data=dtmp[which(dtmp$check_fail==0),])
## Marginal Effect of Ideology on Easing Opinion
fm1h2xmg <- data.frame(
tr = c(0,1), ide="自己申告", mod=c(1,1,2,2),
rbind(coeftest(fm1h2xb0_1, vcovHC(fm1h2xb0_1, "HC2"))[3,],
coeftest(fm1h2xb1_1, vcovHC(fm1h2xb1_1, "HC2"))[3,],
coeftest(fm1h2xb0_2, vcovHC(fm1h2xb0_2, "HC2"))[3,],
coeftest(fm1h2xb1_2, vcovHC(fm1h2xb1_2, "HC2"))[3,])
)
names(fm1h2xmg)[4:7] <- c("est","se","tval","pval")# Main Models (With Different Base Category)
m2h2xb0_1 <- lm(easing_opi ~ g_h2x*ide_psup, data=dtmp)
m2h2xb0_2 <- lm(update(easing_opi ~ g_h2x*ide_psup,ctl), data=dtmp)
m2h2xb1_1 <- lm(easing_opi ~ I(1-g_h2x)*ide_psup, data=dtmp)
m2h2xb1_2 <- lm(update(easing_opi ~ I(1-g_h2x)*ide_psup,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
m2h2xmg <- data.frame(
tr = c(0,1), ide="政党支持", mod=c(1,1,2,2),
rbind(coeftest(m2h2xb0_1, vcovHC(m2h2xb0_1, "HC2"))[3,],
coeftest(m2h2xb1_1, vcovHC(m2h2xb1_1, "HC2"))[3,],
coeftest(m2h2xb0_2, vcovHC(m2h2xb0_2, "HC2"))[3,],
coeftest(m2h2xb1_2, vcovHC(m2h2xb1_2, "HC2"))[3,])
)
names(m2h2xmg)[4:7] <- c("est","se","tval","pval")
# Models with Missing Values as NA
nm2h2xb0_1 <- lm(easing_opi_mis ~ g_h2x*ide_psup_mis, data=dtmp)
nm2h2xb0_2 <- lm(update(easing_opi_mis ~ g_h2x*ide_psup_mis,ctl), data=dtmp)
nm2h2xb1_1 <- lm(easing_opi_mis ~ I(1-g_h2x)*ide_psup_mis, data=dtmp)
nm2h2xb1_2 <- lm(update(easing_opi_mis ~ I(1-g_h2x)*ide_psup_mis,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
nm2h2xmg <- data.frame(
tr = c(0,1), ide="政党支持", mod=c(1,1,2,2),
rbind(coeftest(nm2h2xb0_1, vcovHC(nm2h2xb0_1, "HC2"))[3,],
coeftest(nm2h2xb1_1, vcovHC(nm2h2xb1_1, "HC2"))[3,],
coeftest(nm2h2xb0_2, vcovHC(nm2h2xb0_2, "HC2"))[3,],
coeftest(nm2h2xb1_2, vcovHC(nm2h2xb1_2, "HC2"))[3,])
)
names(nm2h2xmg)[4:7] <- c("est","se","tval","pval")
# Models without Respondents Failing Manipulation Check
fm2h2xb0_1 <- lm(easing_opi ~ g_h2x*ide_psup, data=dtmp[which(dtmp$check_fail==0),])
fm2h2xb0_2 <- lm(update(easing_opi ~ g_h2x*ide_psup,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm2h2xb1_1 <- lm(easing_opi ~ I(1-g_h2x)*ide_psup, data=dtmp[which(dtmp$check_fail==0),])
fm2h2xb1_2 <- lm(update(easing_opi ~ I(1-g_h2x)*ide_psup,ctl), data=dtmp[which(dtmp$check_fail==0),])
## Marginal Effect of Ideology on Easing Opinion
fm2h2xmg <- data.frame(
tr = c(0,1), ide="政党支持", mod=c(1,1,2,2),
rbind(coeftest(fm2h2xb0_1, vcovHC(fm2h2xb0_1, "HC2"))[3,],
coeftest(fm2h2xb1_1, vcovHC(fm2h2xb1_1, "HC2"))[3,],
coeftest(fm2h2xb0_2, vcovHC(fm2h2xb0_2, "HC2"))[3,],
coeftest(fm2h2xb1_2, vcovHC(fm2h2xb1_2, "HC2"))[3,])
)
names(fm2h2xmg)[4:7] <- c("est","se","tval","pval")# Main Models (With Different Base Category)
m3h2xb0_1 <- lm(easing_opi ~ g_h2x*ide_iss_1, data=dtmp)
m3h2xb0_2 <- lm(update(easing_opi ~ g_h2x*ide_iss_1,ctl), data=dtmp)
m3h2xb1_1 <- lm(easing_opi ~ I(1-g_h2x)*ide_iss_1, data=dtmp)
m3h2xb1_2 <- lm(update(easing_opi ~ I(1-g_h2x)*ide_iss_1,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
m3h2xmg <- data.frame(
tr = c(0,1), ide="外交安全保障", mod=c(1,1,2,2),
rbind(coeftest(m3h2xb0_1, vcovHC(m3h2xb0_1, "HC2"))[3,],
coeftest(m3h2xb1_1, vcovHC(m3h2xb1_1, "HC2"))[3,],
coeftest(m3h2xb0_2, vcovHC(m3h2xb0_2, "HC2"))[3,],
coeftest(m3h2xb1_2, vcovHC(m3h2xb1_2, "HC2"))[3,])
)
names(m3h2xmg)[4:7] <- c("est","se","tval","pval")
# Models with Missing Values as NA
nm3h2xb0_1 <- lm(easing_opi_mis ~ g_h2x*ide_iss_mis_1, data=dtmp)
nm3h2xb0_2 <- lm(update(easing_opi_mis ~ g_h2x*ide_iss_mis_1,ctl), data=dtmp)
nm3h2xb1_1 <- lm(easing_opi_mis ~ I(1-g_h2x)*ide_iss_mis_1, data=dtmp)
nm3h2xb1_2 <- lm(update(easing_opi_mis ~ I(1-g_h2x)*ide_iss_mis_1,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
nm3h2xmg <- data.frame(
tr = c(0,1), ide="外交安全保障", mod=c(1,1,2,2),
rbind(coeftest(nm3h2xb0_1, vcovHC(nm3h2xb0_1, "HC2"))[3,],
coeftest(nm3h2xb1_1, vcovHC(nm3h2xb1_1, "HC2"))[3,],
coeftest(nm3h2xb0_2, vcovHC(nm3h2xb0_2, "HC2"))[3,],
coeftest(nm3h2xb1_2, vcovHC(nm3h2xb1_2, "HC2"))[3,])
)
names(nm3h2xmg)[4:7] <- c("est","se","tval","pval")
# Models without Respondents Failing Manipulation Check
fm3h2xb0_1 <- lm(easing_opi ~ g_h2x*ide_iss_1, data=dtmp[which(dtmp$check_fail==0),])
fm3h2xb0_2 <- lm(update(easing_opi ~ g_h2x*ide_iss_1,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm3h2xb1_1 <- lm(easing_opi ~ I(1-g_h2x)*ide_iss_1, data=dtmp[which(dtmp$check_fail==0),])
fm3h2xb1_2 <- lm(update(easing_opi ~ I(1-g_h2x)*ide_iss_1,ctl), data=dtmp[which(dtmp$check_fail==0),])
## Marginal Effect of Ideology on Easing Opinion
fm3h2xmg <- data.frame(
tr = c(0,1), ide="外交安全保障", mod=c(1,1,2,2),
rbind(coeftest(fm3h2xb0_1, vcovHC(fm3h2xb0_1, "HC2"))[3,],
coeftest(fm3h2xb1_1, vcovHC(fm3h2xb1_1, "HC2"))[3,],
coeftest(fm3h2xb0_2, vcovHC(fm3h2xb0_2, "HC2"))[3,],
coeftest(fm3h2xb1_2, vcovHC(fm3h2xb1_2, "HC2"))[3,])
)
names(fm3h2xmg)[4:7] <- c("est","se","tval","pval")# Main Models (With Different Base Category)
m4h2xb0_1 <- lm(easing_opi ~ g_h2x*ide_iss_2, data=dtmp)
m4h2xb0_2 <- lm(update(easing_opi ~ g_h2x*ide_iss_2,ctl), data=dtmp)
m4h2xb1_1 <- lm(easing_opi ~ I(1-g_h2x)*ide_iss_2, data=dtmp)
m4h2xb1_2 <- lm(update(easing_opi ~ I(1-g_h2x)*ide_iss_2,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
m4h2xmg <- data.frame(
tr = c(0,1), ide="権利機会平等", mod=c(1,1,2,2),
rbind(coeftest(m4h2xb0_1, vcovHC(m4h2xb0_1, "HC2"))[3,],
coeftest(m4h2xb1_1, vcovHC(m4h2xb1_1, "HC2"))[3,],
coeftest(m4h2xb0_2, vcovHC(m4h2xb0_2, "HC2"))[3,],
coeftest(m4h2xb1_2, vcovHC(m4h2xb1_2, "HC2"))[3,])
)
names(m4h2xmg)[4:7] <- c("est","se","tval","pval")
# Models with Missing Values as NA
nm4h2xb0_1 <- lm(easing_opi_mis ~ g_h2x*ide_iss_mis_2, data=dtmp)
nm4h2xb0_2 <- lm(update(easing_opi_mis ~ g_h2x*ide_iss_mis_2,ctl), data=dtmp)
nm4h2xb1_1 <- lm(easing_opi_mis ~ I(1-g_h2x)*ide_iss_mis_2, data=dtmp)
nm4h2xb1_2 <- lm(update(easing_opi_mis ~ I(1-g_h2x)*ide_iss_mis_2,ctl), data=dtmp)
## Marginal Effect of Ideology on Easing Opinion
nm4h2xmg <- data.frame(
tr = c(0,1), ide="権利機会平等", mod=c(1,1,2,2),
rbind(coeftest(nm4h2xb0_1, vcovHC(nm4h2xb0_1, "HC2"))[3,],
coeftest(nm4h2xb1_1, vcovHC(nm4h2xb1_1, "HC2"))[3,],
coeftest(nm4h2xb0_2, vcovHC(nm4h2xb0_2, "HC2"))[3,],
coeftest(nm4h2xb1_2, vcovHC(nm4h2xb1_2, "HC2"))[3,])
)
names(nm4h2xmg)[4:7] <- c("est","se","tval","pval")
# Models without Respondents Failing Manipulation Check
fm4h2xb0_1 <- lm(easing_opi ~ g_h2x*ide_iss_2, data=dtmp[which(dtmp$check_fail==0),])
fm4h2xb0_2 <- lm(update(easing_opi ~ g_h2x*ide_iss_2,ctl), data=dtmp[which(dtmp$check_fail==0),])
fm4h2xb1_1 <- lm(easing_opi ~ I(1-g_h2x)*ide_iss_2, data=dtmp[which(dtmp$check_fail==0),])
fm4h2xb1_2 <- lm(update(easing_opi ~ I(1-g_h2x)*ide_iss_2,ctl), data=dtmp[which(dtmp$check_fail==0),])
## Marginal Effect of Ideology on Easing Opinion
fm4h2xmg <- data.frame(
tr = c(0,1), ide="権利機会平等", mod=c(1,1,2,2),
rbind(coeftest(fm4h2xb0_1, vcovHC(fm4h2xb0_1, "HC2"))[3,],
coeftest(fm4h2xb1_1, vcovHC(fm4h2xb1_1, "HC2"))[3,],
coeftest(fm4h2xb0_2, vcovHC(fm4h2xb0_2, "HC2"))[3,],
coeftest(fm4h2xb1_2, vcovHC(fm4h2xb1_2, "HC2"))[3,])
)
names(fm4h2xmg)[4:7] <- c("est","se","tval","pval")# Combine Outputs
mh2x_mgdt <- rbind(m1h2xmg,m2h2xmg,m3h2xmg,m4h2xmg)
nmh2x_mgdt <- rbind(nm1h2xmg,nm2h2xmg,nm3h2xmg,nm4h2xmg)
fmh2x_mgdt <- rbind(fm1h2xmg,fm2h2xmg,fm3h2xmg,fm4h2xmg)
# 95% Confidence Intervals
mh2x_mgdt$lci95 <- mh2x_mgdt$est - qnorm(0.975)*mh2x_mgdt$se
mh2x_mgdt$uci95 <- mh2x_mgdt$est + qnorm(0.975)*mh2x_mgdt$se
nmh2x_mgdt$lci95 <- nmh2x_mgdt$est - qnorm(0.975)*nmh2x_mgdt$se
nmh2x_mgdt$uci95 <- nmh2x_mgdt$est + qnorm(0.975)*nmh2x_mgdt$se
fmh2x_mgdt$lci95 <- fmh2x_mgdt$est - qnorm(0.975)*fmh2x_mgdt$se
fmh2x_mgdt$uci95 <- fmh2x_mgdt$est + qnorm(0.975)*fmh2x_mgdt$se
# 90% Confidence Intervals
mh2x_mgdt$lci90 <- mh2x_mgdt$est - qnorm(0.95)*mh2x_mgdt$se
mh2x_mgdt$uci90 <- mh2x_mgdt$est + qnorm(0.95)*mh2x_mgdt$se
nmh2x_mgdt$lci90 <- nmh2x_mgdt$est - qnorm(0.95)*nmh2x_mgdt$se
nmh2x_mgdt$uci90 <- nmh2x_mgdt$est + qnorm(0.95)*nmh2x_mgdt$se
fmh2x_mgdt$lci90 <- fmh2x_mgdt$est - qnorm(0.95)*fmh2x_mgdt$se
fmh2x_mgdt$uci90 <- fmh2x_mgdt$est + qnorm(0.95)*fmh2x_mgdt$se
# P-test Thresholds
mh2x_mgdt$ptest <- ifelse(mh2x_mgdt$pval<0.05,"p<.05",
ifelse(mh2x_mgdt$pval<0.1,"p<.10","n.s.(p>=.10)"))
mh2x_mgdt$ptest <- factor(mh2x_mgdt$ptest,
levels=c("p<.05","p<.10","n.s.(p>=.10)"))
nmh2x_mgdt$ptest <- ifelse(nmh2x_mgdt$pval<0.05,"p<.05",
ifelse(nmh2x_mgdt$pval<0.1,"p<.10","n.s.(p>=.10)"))
nmh2x_mgdt$ptest <- factor(nmh2x_mgdt$ptest,
levels=c("p<.05","p<.10","n.s.(p>=.10)"))
fmh2x_mgdt$ptest <- ifelse(fmh2x_mgdt$pval<0.05,"p<.05",
ifelse(fmh2x_mgdt$pval<0.1,"p<.10","n.s.(p>=.10)"))
fmh2x_mgdt$ptest <- factor(fmh2x_mgdt$ptest,
levels=c("p<.05","p<.10","n.s.(p>=.10)"))
# Make Other Variables Factor
mh2x_mgdt$tr <- factor(mh2x_mgdt$tr, levels=unique(mh2x_mgdt$tr),
labels=c("1.経済成長","2.経済成長\n&格差縮小"))
mh2x_mgdt$ide <- factor(mh2x_mgdt$ide, levels=unique(mh2x_mgdt$ide))
nmh2x_mgdt$tr <- factor(nmh2x_mgdt$tr, levels=unique(nmh2x_mgdt$tr),
labels=c("1.経済成長","2.経済成長\n&格差縮小"))
nmh2x_mgdt$ide <- factor(nmh2x_mgdt$ide, levels=unique(nmh2x_mgdt$ide))
fmh2x_mgdt$tr <- factor(fmh2x_mgdt$tr, levels=unique(fmh2x_mgdt$tr),
labels=c("1.経済成長","2.経済成長\n&格差縮小"))
fmh2x_mgdt$ide <- factor(fmh2x_mgdt$ide, levels=unique(fmh2x_mgdt$ide))# H2X
h2xcf <- rbind(coeftest(m1h2xb0_1, vcovHC(m1h2xb0_1, "HC2"))[4,],
coeftest(m2h2xb0_1, vcovHC(m2h2xb0_1, "HC2"))[4,],
coeftest(m3h2xb0_1, vcovHC(m3h2xb0_1, "HC2"))[4,],
coeftest(m4h2xb0_1, vcovHC(m4h2xb0_1, "HC2"))[4,],
coeftest(m1h2xb0_2, vcovHC(m1h2xb0_2, "HC2"))[14,],
coeftest(m2h2xb0_2, vcovHC(m2h2xb0_2, "HC2"))[14,],
coeftest(m3h2xb0_2, vcovHC(m3h2xb0_2, "HC2"))[14,],
coeftest(m4h2xb0_2, vcovHC(m4h2xb0_2, "HC2"))[14,],
coeftest(nm1h2xb0_1, vcovHC(nm1h2xb0_1, "HC2"))[4,],
coeftest(nm2h2xb0_1, vcovHC(nm2h2xb0_1, "HC2"))[4,],
coeftest(nm3h2xb0_1, vcovHC(nm3h2xb0_1, "HC2"))[4,],
coeftest(nm4h2xb0_1, vcovHC(nm4h2xb0_1, "HC2"))[4,],
coeftest(nm1h2xb0_2, vcovHC(nm1h2xb0_2, "HC2"))[14,],
coeftest(nm2h2xb0_2, vcovHC(nm2h2xb0_2, "HC2"))[14,],
coeftest(nm3h2xb0_2, vcovHC(nm3h2xb0_2, "HC2"))[14,],
coeftest(nm4h2xb0_2, vcovHC(nm4h2xb0_2, "HC2"))[14,],
coeftest(fm1h2xb0_1, vcovHC(fm1h2xb0_1, "HC2"))[4,],
coeftest(fm2h2xb0_1, vcovHC(fm2h2xb0_1, "HC2"))[4,],
coeftest(fm3h2xb0_1, vcovHC(fm3h2xb0_1, "HC2"))[4,],
coeftest(fm4h2xb0_1, vcovHC(fm4h2xb0_1, "HC2"))[4,],
coeftest(fm1h2xb0_2, vcovHC(fm1h2xb0_2, "HC2"))[14,],
coeftest(fm2h2xb0_2, vcovHC(fm2h2xb0_2, "HC2"))[14,],
coeftest(fm3h2xb0_2, vcovHC(fm3h2xb0_2, "HC2"))[14,],
coeftest(fm4h2xb0_2, vcovHC(fm4h2xb0_2, "HC2"))[14,])
colnames(h2xcf) <- c("est","se","tval","pval")
h2xcf <- as.data.frame(h2xcf)
h2xcf$meth <- rep(c("main_base","main_ext",
"mis_base","mis_ext",
"dropfail_base","dropfail_ext"), each=4)
h2xcf$meth <- factor(h2xcf$meth, levels=unique(h2xcf$meth),
labels = c("「わからない」回答=0;\n統制変数無",
"「わからない」回答=0;\n統制変数有",
"「わからない」回答除外;\n統制変数無",
"「わからない」回答除外;\n統制変数有",
"マニピュレーション\nチェック違反除外;\n統制変数無",
"マニピュレーション\nチェック違反除外;\n統制変数有" ))
h2xcf$meth <- factor(h2xcf$meth, levels=rev(unique(h2xcf$meth)))
h2xcf$hyp <- "H2X:2.経済成長&格差縮小\nv.s.1.経済成長"
h2xcf$hyp <- factor(h2xcf$hyp, levels=rev(unique(h2xcf$hyp)))
h2xcf$ms <- c("自己申告","政党支持","外交安全保障","権利機会平等")
h2xcf$ms <- factor(h2xcf$ms, levels=unique(h2xcf$ms))
h2xcf$l90CI <- h2xcf$est - h2xcf$se*qnorm(0.95)
h2xcf$u90CI <- h2xcf$est + h2xcf$se*qnorm(0.95)
h2xcf$l95CI <- h2xcf$est - h2xcf$se*qnorm(0.975)
h2xcf$u95CI <- h2xcf$est + h2xcf$se*qnorm(0.975)
h2xcf$ptest <- ifelse(h2xcf$pval<0.05,"p<.05",
ifelse(h2xcf$pval<0.10,"p<.10","n.s.(p>=.10)"))
h2xcf$ptest <- factor(h2xcf$ptest, levels=c("p<.05","p<.10","n.s.(p>=.10)"))exporth2xplot <- function(captiontxt) {
p <- ggplot(h2xcf, aes_string(x="meth")) +
geom_hline(aes(yintercept=0), linetype=2) +
geom_errorbar(aes_string(ymin="l95CI",ymax="u95CI",
color="ptest"),width=0.1) +
geom_errorbar(aes_string(ymin="l90CI",ymax="u90CI",
color="ptest"),width=0,size=0.8) +
geom_point(aes_string(y="est",color="ptest",shape="ptest"),size=2) +
scale_y_continuous(breaks=c(-0.3,0,0.3)) +
scale_color_manual(name="", values=c("red2","darkorange2","gray50"),
drop=FALSE) +
scale_shape_discrete(name="", drop=FALSE) +
facet_grid(.~ms) +
xlab(NULL) +
ylab("イデオロギー交差項の係数+95%信頼区間\n(太線は90%信頼区間)") +
labs(subtitle="イデオロギー指標",
caption=captiontxt) +
coord_flip() + theme_bw() +
theme(plot.margin = unit(c(0.5,0.5,0.5,0.5), "cm"),
panel.grid = element_line(color=NA),
plot.subtitle = element_text(hjust=0.5),
axis.text.y = element_text(color="black"),
legend.position = "bottom")
p
}table_coef(list(m1h2xb0_1,m2h2xb0_1,m3h2xb0_1,m4h2xb0_1),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
custom.variable.names = c(vn_ext[c(1,4)],rep(vn_idex_ext[c(1,4)],4)),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="格差縮小フレームが金融緩和選好に与える効果に対するイデオロギーの条件付け(統制変数無;金融緩和選好とイデオロギー変数の「わからない」回答には0を代入)",
label="idetab_h2x_1", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).",
format = "tex", file.name = "../out/v6_idetab_h2x_1")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT g_h2x 2X.経済成長&格差縮小
## KEPT ide_self イデオロギー
## KEPT g_h2x:ide_self イデオロギー×2X.成長&格差
## KEPT ide_psup イデオロギー
## KEPT g_h2x:ide_psup イデオロギー×2X.成長&格差
## KEPT ide_iss_1 イデオロギー
## KEPT g_h2x:ide_iss_1 イデオロギー×2X.成長&格差
## KEPT ide_iss_2 イデオロギー
## KEPT g_h2x:ide_iss_2 イデオロギー×2X.成長&格差
## The table was written to the file '../out/v6_idetab_h2x_1.tex'.
##
## ===================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## -------------------------------------------------------------------
## (定数項) 0.883 *** 0.861 *** 0.899 *** 0.899 ***
## (0.098) (0.098) (0.091) (0.098)
## 2X.経済成長&格差縮小 0.001 -0.020 0.002 -0.022
## (0.131) (0.133) (0.123) (0.128)
## イデオロギー 0.217 * 0.256 + 0.465 *** -0.106
## (0.091) (0.135) (0.101) (0.100)
## イデオロギー×2X.成長&格差 -0.169 0.006 -0.279 * -0.013
## (0.130) (0.182) (0.137) (0.126)
## -------------------------------------------------------------------
## R^2 0.019 0.024 0.078 0.011
## Adj. R^2 0.012 0.016 0.071 0.003
## Num. obs. 381 381 381 381
## RMSE 1.241 1.238 1.203 1.246
## ===================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).
tmp <- readLines("../out/v6_idetab_h2x_1.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_idetab_h2x_1.tex", useBytes = TRUE)table_coef(list(m1h2xb0_2,m2h2xb0_2,m3h2xb0_2,m4h2xb0_2),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
custom.variable.names = vnx_ext[-c(2:3,5:6,18:19,21:22,24:25,27:28,30:31,33:34,36:37,39:40)],
order.variable = c(1:3,14:20,4:13),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="格差縮小フレームが金融緩和選好に与える効果に対するイデオロギーの条件付け(統制変数有;金融緩和選好とイデオロギー変数の「わからない」回答には0を代入)",
label="idetab_h2x_2", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).",
format = "tex", file.name = "../out/v6_idetab_h2x_2")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT g_h2x 2X.経済成長&格差縮小
## KEPT ide_self イデオロギー
## KEPT knall 政治知識
## KEPT fem 性別(女性)
## KEPT age 年齢
## KEPT lvlen 居住年数
## KEPT ownh 持ち家
## KEPT as.factor(edu3)1 教育:短大/高専/専門学校
## KEPT as.factor(edu3)2 教育:大卒以上
## KEPT wk 就労
## KEPT mar 婚姻
## KEPT cld 子ども
## KEPT g_h2x:ide_self イデオロギー×2X.成長&格差
## KEPT ide_psup イデオロギー
## KEPT g_h2x:ide_psup イデオロギー×2X.成長&格差
## KEPT ide_iss_1 イデオロギー
## KEPT g_h2x:ide_iss_1 イデオロギー×2X.成長&格差
## KEPT ide_iss_2 イデオロギー
## KEPT g_h2x:ide_iss_2 イデオロギー×2X.成長&格差
## The table was written to the file '../out/v6_idetab_h2x_2.tex'.
##
## ===================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## -------------------------------------------------------------------
## (定数項) 1.441 *** 1.398 *** 1.387 *** 1.406 ***
## (0.317) (0.314) (0.303) (0.321)
## 2X.経済成長&格差縮小 -0.004 -0.026 0.000 -0.029
## (0.131) (0.133) (0.123) (0.128)
## イデオロギー 0.200 * 0.212 0.449 *** -0.121
## (0.089) (0.139) (0.103) (0.101)
## イデオロギー×2X.成長&格差 -0.153 0.042 -0.289 * 0.008
## (0.127) (0.189) (0.136) (0.125)
## 政治知識 0.060 0.035 -0.043 0.057
## (0.261) (0.271) (0.257) (0.269)
## 性別(女性) -0.184 -0.185 -0.116 -0.275 *
## (0.139) (0.140) (0.139) (0.137)
## 年齢 -0.015 * -0.014 * -0.013 * -0.013 +
## (0.007) (0.007) (0.007) (0.007)
## 居住年数 -0.050 -0.040 -0.050 -0.050
## (0.054) (0.054) (0.053) (0.054)
## 持ち家 0.080 0.073 0.096 0.118
## (0.142) (0.142) (0.136) (0.144)
## 教育:短大/高専/専門学校 -0.042 -0.075 -0.012 -0.093
## (0.225) (0.229) (0.220) (0.224)
## 教育:大卒以上 0.010 0.016 0.050 -0.003
## (0.185) (0.189) (0.180) (0.187)
## 就労 0.126 0.118 0.093 0.130
## (0.142) (0.143) (0.140) (0.143)
## 婚姻 -0.053 -0.106 -0.078 -0.037
## (0.208) (0.209) (0.204) (0.207)
## 子ども 0.151 0.169 0.169 0.168
## (0.205) (0.208) (0.204) (0.208)
## -------------------------------------------------------------------
## R^2 0.047 0.050 0.098 0.041
## Adj. R^2 0.013 0.016 0.066 0.007
## Num. obs. 381 381 381 381
## RMSE 1.240 1.238 1.206 1.244
## ===================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).
tmp <- readLines("../out/v6_idetab_h2x_2.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_idetab_h2x_2.tex", useBytes = TRUE)table_coef(list(nm1h2xb0_1,nm2h2xb0_1,nm3h2xb0_1,nm4h2xb0_1),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
custom.variable.names = c(vn_ext[c(1,4)],rep(vn_idex_ext[c(1,4)],4)),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="格差縮小フレームが金融緩和選好に与える効果に対するイデオロギーの条件付け(統制変数無;金融緩和選好とイデオロギー変数の「わからない」回答は分析から除外)",
label="idetab_h2x_n1", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).",
format = "tex", file.name = "../out/v6_idetab_h2x_n1")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT g_h2x 2X.経済成長&格差縮小
## KEPT ide_self_mis イデオロギー
## KEPT g_h2x:ide_self_mis イデオロギー×2X.成長&格差
## KEPT ide_psup_mis イデオロギー
## KEPT g_h2x:ide_psup_mis イデオロギー×2X.成長&格差
## KEPT ide_iss_mis_1 イデオロギー
## KEPT g_h2x:ide_iss_mis_1 イデオロギー×2X.成長&格差
## KEPT ide_iss_mis_2 イデオロギー
## KEPT g_h2x:ide_iss_mis_2 イデオロギー×2X.成長&格差
## The table was written to the file '../out/v6_idetab_h2x_n1.tex'.
##
## ===================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## -------------------------------------------------------------------
## (定数項) 1.053 *** 0.972 *** 0.931 *** 0.927 ***
## (0.114) (0.105) (0.132) (0.146)
## 2X.経済成長&格差縮小 -0.038 -0.021 0.039 0.042
## (0.154) (0.147) (0.173) (0.187)
## イデオロギー 0.240 ** 0.291 * 0.623 *** 0.017
## (0.090) (0.146) (0.150) (0.144)
## イデオロギー×2X.成長&格差 -0.187 -0.073 -0.289 -0.160
## (0.129) (0.199) (0.201) (0.178)
## -------------------------------------------------------------------
## R^2 0.029 0.023 0.136 0.009
## Adj. R^2 0.019 0.015 0.124 -0.006
## Num. obs. 289 337 210 210
## RMSE 1.271 1.264 1.243 1.332
## ===================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).
tmp <- readLines("../out/v6_idetab_h2x_n1.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_idetab_h2x_n1.tex", useBytes = TRUE)table_coef(list(nm1h2xb0_2,nm2h2xb0_2,nm3h2xb0_2,nm4h2xb0_2),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
custom.variable.names = vnx_ext[-c(2:3,5:6,18:19,21:22,24:25,27:28,30:31,33:34,36:37,39:40)],
order.variable = c(1:3,14:20,4:13),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="格差縮小フレームが金融緩和選好に与える効果に対するイデオロギーの条件付け(統制変数有;金融緩和選好とイデオロギー変数の「わからない」回答は分析から除外)",
label="idetab_h2x_n2", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).",
format = "tex", file.name = "../out/v6_idetab_h2x_n2")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT g_h2x 2X.経済成長&格差縮小
## KEPT ide_self_mis イデオロギー
## KEPT knall 政治知識
## KEPT fem 性別(女性)
## KEPT age 年齢
## KEPT lvlen 居住年数
## KEPT ownh 持ち家
## KEPT as.factor(edu3)1 教育:短大/高専/専門学校
## KEPT as.factor(edu3)2 教育:大卒以上
## KEPT wk 就労
## KEPT mar 婚姻
## KEPT cld 子ども
## KEPT g_h2x:ide_self_mis イデオロギー×2X.成長&格差
## KEPT ide_psup_mis イデオロギー
## KEPT g_h2x:ide_psup_mis イデオロギー×2X.成長&格差
## KEPT ide_iss_mis_1 イデオロギー
## KEPT g_h2x:ide_iss_mis_1 イデオロギー×2X.成長&格差
## KEPT ide_iss_mis_2 イデオロギー
## KEPT g_h2x:ide_iss_mis_2 イデオロギー×2X.成長&格差
## The table was written to the file '../out/v6_idetab_h2x_n2.tex'.
##
## ==================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## ------------------------------------------------------------------
## (定数項) 1.940 *** 1.605 *** 1.289 ** 1.382 **
## (0.376) (0.349) (0.446) (0.479)
## 2X.経済成長&格差縮小 -0.045 -0.038 0.080 0.080
## (0.155) (0.146) (0.173) (0.184)
## イデオロギー 0.216 * 0.225 0.588 *** -0.017
## (0.087) (0.153) (0.145) (0.147)
## イデオロギー×2X.成長&格差 -0.173 -0.008 -0.313 -0.131
## (0.124) (0.206) (0.192) (0.179)
## 政治知識 -0.317 -0.152 -0.104 0.050
## (0.299) (0.292) (0.372) (0.386)
## 性別(女性) -0.261 -0.224 -0.286 -0.490 **
## (0.163) (0.155) (0.182) (0.174)
## 年齢 -0.013 -0.011 -0.013 -0.014
## (0.009) (0.007) (0.009) (0.010)
## 居住年数 -0.104 -0.069 -0.103 -0.111
## (0.066) (0.061) (0.069) (0.075)
## 持ち家 0.189 0.089 0.081 0.146
## (0.166) (0.159) (0.185) (0.206)
## 教育:短大/高専/専門学校 -0.249 -0.130 0.010 -0.213
## (0.273) (0.246) (0.346) (0.367)
## 教育:大卒以上 -0.128 0.016 0.147 0.045
## (0.228) (0.209) (0.295) (0.322)
## 就労 0.149 0.131 0.449 * 0.474 *
## (0.167) (0.155) (0.194) (0.211)
## 婚姻 -0.112 -0.166 0.163 0.185
## (0.270) (0.224) (0.313) (0.326)
## 子ども 0.140 0.152 -0.145 -0.137
## (0.260) (0.220) (0.306) (0.317)
## ------------------------------------------------------------------
## R^2 0.076 0.055 0.202 0.106
## Adj. R^2 0.032 0.017 0.149 0.047
## Num. obs. 289 337 210 210
## RMSE 1.263 1.262 1.225 1.296
## ==================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).
tmp <- readLines("../out/v6_idetab_h2x_n2.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_idetab_h2x_n2.tex", useBytes = TRUE)table_coef(list(fm1h2xb0_1,fm2h2xb0_1,fm3h2xb0_1,fm4h2xb0_1),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
custom.variable.names = c(vn_ext[c(1,4)],rep(vn_idex_ext[c(1,4)],4)),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="格差縮小フレームが金融緩和選好に与える効果に対するイデオロギーの条件付け(統制変数無;マニピュレーションチェックに違反した回答者を分析から除外)",
label="idetab_h2x_f1", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).",
format = "tex", file.name = "../out/v6_idetab_h2x_f1")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT g_h2x 2X.経済成長&格差縮小
## KEPT ide_self イデオロギー
## KEPT g_h2x:ide_self イデオロギー×2X.成長&格差
## KEPT ide_psup イデオロギー
## KEPT g_h2x:ide_psup イデオロギー×2X.成長&格差
## KEPT ide_iss_1 イデオロギー
## KEPT g_h2x:ide_iss_1 イデオロギー×2X.成長&格差
## KEPT ide_iss_2 イデオロギー
## KEPT g_h2x:ide_iss_2 イデオロギー×2X.成長&格差
## The table was written to the file '../out/v6_idetab_h2x_f1.tex'.
##
## ===================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## -------------------------------------------------------------------
## (定数項) 0.811 *** 0.793 *** 0.833 *** 0.820 ***
## (0.101) (0.101) (0.094) (0.101)
## 2X.経済成長&格差縮小 0.047 0.011 0.046 0.035
## (0.139) (0.142) (0.131) (0.137)
## イデオロギー 0.234 * 0.244 + 0.464 *** -0.148
## (0.095) (0.140) (0.106) (0.108)
## イデオロギー×2X.成長&格差 -0.170 0.055 -0.310 * 0.049
## (0.136) (0.194) (0.147) (0.138)
## -------------------------------------------------------------------
## R^2 0.024 0.026 0.078 0.012
## Adj. R^2 0.015 0.017 0.069 0.003
## Num. obs. 333 333 333 333
## RMSE 1.241 1.240 1.207 1.249
## ===================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).
tmp <- readLines("../out/v6_idetab_h2x_f1.tex")
# tmp <- iconv(tmp, from="SHIFT-JIS", to="UTF-8")
writeLines(tmp, "../out/v6_idetab_h2x_f1.tex", useBytes = TRUE)table_coef(list(fm1h2xb0_2,fm2h2xb0_2,fm3h2xb0_2,fm4h2xb0_2),
vcov.est="robust", robust.type="HC2",
single.row=FALSE,
custom.variable.names = vnx_ext[-c(2:3,5:6,18:19,21:22,24:25,27:28,30:31,33:34,36:37,39:40)],
order.variable = c(1:3,14:20,4:13),
m.names = c("自己申告","政党支持","外交安全保障","権利機会平等"),
caption="格差縮小フレームが金融緩和選好に与える効果に対するイデオロギーの条件付け(統制変数有;マニピュレーションチェックに違反した回答者を分析から除外)",
label="idetab_h2x_f2", dcolumn = TRUE, fontsize = "scriptsize", float.pos = "ht!!",
custom.footnote = "最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).",
format = "tex", file.name = "../out/v6_idetab_h2x_f2")## Variable Manipulations:
## Omitted Original Final
## KEPT (Intercept) (定数項)
## KEPT g_h2x 2X.経済成長&格差縮小
## KEPT ide_self イデオロギー
## KEPT knall 政治知識
## KEPT fem 性別(女性)
## KEPT age 年齢
## KEPT lvlen 居住年数
## KEPT ownh 持ち家
## KEPT as.factor(edu3)1 教育:短大/高専/専門学校
## KEPT as.factor(edu3)2 教育:大卒以上
## KEPT wk 就労
## KEPT mar 婚姻
## KEPT cld 子ども
## KEPT g_h2x:ide_self イデオロギー×2X.成長&格差
## KEPT ide_psup イデオロギー
## KEPT g_h2x:ide_psup イデオロギー×2X.成長&格差
## KEPT ide_iss_1 イデオロギー
## KEPT g_h2x:ide_iss_1 イデオロギー×2X.成長&格差
## KEPT ide_iss_2 イデオロギー
## KEPT g_h2x:ide_iss_2 イデオロギー×2X.成長&格差
## The table was written to the file '../out/v6_idetab_h2x_f2.tex'.
##
## ===================================================================
## 自己申告 政党支持 外交安全保障 権利機会平等
## -------------------------------------------------------------------
## (定数項) 1.320 *** 1.262 *** 1.226 *** 1.251 ***
## (0.340) (0.336) (0.323) (0.342)
## 2X.経済成長&格差縮小 0.054 0.020 0.056 0.037
## (0.140) (0.144) (0.133) (0.138)
## イデオロギー 0.225 * 0.215 0.445 *** -0.157
## (0.092) (0.144) (0.107) (0.110)
## イデオロギー×2X.成長&格差 -0.158 0.076 -0.311 * 0.051
## (0.136) (0.206) (0.147) (0.139)
## 政治知識 0.078 0.053 -0.015 0.066
## (0.283) (0.294) (0.280) (0.292)
## 性別(女性) -0.157 -0.140 -0.085 -0.253 +
## (0.148) (0.149) (0.148) (0.147)
## 年齢 -0.011 -0.009 -0.010 -0.009
## (0.007) (0.007) (0.007) (0.008)
## 居住年数 -0.061 -0.053 -0.062 -0.059
## (0.058) (0.058) (0.056) (0.058)
## 持ち家 0.088 0.074 0.126 0.138
## (0.153) (0.154) (0.149) (0.158)
## 教育:短大/高専/専門学校 -0.117 -0.161 -0.072 -0.173
## (0.255) (0.259) (0.248) (0.253)
## 教育:大卒以上 -0.108 -0.081 -0.024 -0.098
## (0.210) (0.216) (0.203) (0.212)
## 就労 0.180 0.154 0.136 0.177
## (0.152) (0.153) (0.150) (0.153)
## 婚姻 -0.115 -0.183 -0.120 -0.102
## (0.213) (0.215) (0.213) (0.213)
## 子ども 0.105 0.112 0.124 0.124
## (0.215) (0.220) (0.216) (0.217)
## -------------------------------------------------------------------
## R^2 0.050 0.049 0.095 0.040
## Adj. R^2 0.011 0.011 0.058 0.001
## Num. obs. 333 333 333 333
## RMSE 1.243 1.244 1.214 1.250
## ===================================================================
## *** p < 0.001, ** p < 0.01, * p < 0.05, + p < 0.1 最小二乗法による重回帰分析.( )内はロバスト標準誤差(HC2).